Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand...Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.展开更多
Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay ...Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).展开更多
A multi-strategy hybrid whale optimization algorithm(MSHWOA)for complex constrained optimization problems is proposed to overcome the drawbacks of easily trapping into local optimum,slow convergence speed and low opti...A multi-strategy hybrid whale optimization algorithm(MSHWOA)for complex constrained optimization problems is proposed to overcome the drawbacks of easily trapping into local optimum,slow convergence speed and low optimization precision.Firstly,the population is initialized by introducing the theory of good point set,which increases the randomness and diversity of the population and lays the foundation for the global optimization of the algorithm.Then,a novel linearly update equation of convergence factor is designed to coordinate the abilities of exploration and exploitation.At the same time,the global exploration and local exploitation capabilities are improved through the siege mechanism of Harris Hawks optimization algorithm.Finally,the simulation experiments are conducted on the 6 benchmark functions and Wilcoxon rank sum test to evaluate the optimization performance of the improved algorithm.The experimental results show that the proposed algorithm has more significant improvement in optimization accuracy,convergence speed and robustness than the comparison algorithm.展开更多
Various feature selection algorithms are usually employed to improve classification models’overall performance.Optimization algorithms typically accompany such algorithms to select the optimal set of features.Among t...Various feature selection algorithms are usually employed to improve classification models’overall performance.Optimization algorithms typically accompany such algorithms to select the optimal set of features.Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics.The present paper presents two Stages of Local Search models for feature selection based on WOA(Whale Optimization Algorithm)and Great Deluge(GD).GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search.Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm.In addition,disruptive selection(DS)is employed to select the solutions from the population for local search.DS is chosen to maintain the diversity of the population via enhancing low and high-quality solutions.Fifteen(15)standard benchmark datasets provided by the University of California Irvine(UCI)repository were used in evaluating the proposed approaches’performance.Next,a comparison was made with four population-based algorithms as wrapper feature selection methods from the literature.The proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper methods.Hence,the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.展开更多
Image processing,agricultural production,andfield monitoring are essential studies in the researchfield.Plant diseases have an impact on agricultural production and quality.Agricultural disease detection at a preliminar...Image processing,agricultural production,andfield monitoring are essential studies in the researchfield.Plant diseases have an impact on agricultural production and quality.Agricultural disease detection at a preliminary phase reduces economic losses and improves the quality of crops.Manually identifying the agricultural pests is usually evident in plants;also,it takes more time and is an expensive technique.A drone system has been developed to gather photographs over enormous regions such as farm areas and plantations.An atmosphere generates vast amounts of data as it is monitored closely;the evaluation of this big data would increase the production of agricultural production.This paper aims to identify pests in mango trees such as hoppers,mealybugs,inflorescence midges,fruitflies,and stem borers.Because of the massive volumes of large-scale high-dimensional big data collected,it is necessary to reduce the dimensionality of the input for classify-ing images.The community-based cumulative algorithm was used to classify the pests in the existing system.The proposed method uses the Entropy-ELM method with Whale Optimization to improve the classification in detecting pests in agricul-ture.The Entropy-ELM method with the Whale Optimization Algorithm(WOA)is used for feature selection,enhancing mango pests’classification accuracy.Support Vector Machines(SVMs)are especially effective for classifying while users get var-ious classes in which they are interested.They are created as suitable classifiers to categorize any dataset in Big Data effectively.The proposed Entropy-ELM-WOA is more capable compared to the existing systems.展开更多
In this work,an improved active kriging method based on the AK-IS and truncated importance sampling(TIS)method is proposed to efficiently evaluate structural reliability.The novel method called AWK-TIS is inspired by ...In this work,an improved active kriging method based on the AK-IS and truncated importance sampling(TIS)method is proposed to efficiently evaluate structural reliability.The novel method called AWK-TIS is inspired by AK-IS and RBF-GA previously published in the literature.The innovation of the AWK-TIS is that TIS is adopted to lessen the sample pool size significantly,and the whale optimization algorithm(WOA)is employed to acquire the optimal Krigingmodel and themost probable point(MPP).To verify the performance of theAWK-TISmethod for structural reliability,four numerical cases which are utilized as benchmarks in literature and one real engineering problem about a jet van manipulate mechanism are tested.The results indicate the accuracy and efficiency of the proposed method.展开更多
Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used ...Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems.However,with the increased dimensions,higher requirements are put forward for algorithm performance.The double population whale optimization algorithm with distributed collaboration and reverse learning ability(DCRWOA)is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems.In the DCRWOA algorithm,the novel double population search strategy is constructed.Meanwhile,the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search area.Numerical experi-ments are carried out using standard test functions with different dimensions(10,50,100,200).The optimization case of shield construction parameters is also used to test the practical application performance of the proposed algo-rithm.The results show that the DCRWOA algorithm has higher optimization accuracy and stability,and the convergence speed is significantly improved.Therefore,the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.展开更多
Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses.Recent advancements in deep learning(DL)techniques have shown promising results in detecting pavement cracks;howe...Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses.Recent advancements in deep learning(DL)techniques have shown promising results in detecting pavement cracks;however,the selection of relevant features for classification remains challenging.In this study,we propose a new approach for pavement crack detection that integrates deep learning for feature extraction,the whale optimization algorithm(WOA)for feature selection,and random forest(RF)for classification.The performance of the models was evaluated using accuracy,recall,precision,F1 score,and area under the receiver operating characteristic curve(AUC).Our findings reveal that Model 2,which incorporates RF into the ResNet-18 architecture,outperforms baseline Model 1 across all evaluation metrics.Nevertheless,our proposed model,which combines ResNet-18 with both WOA and RF,achieves significantly higher accuracy,recall,precision,and F1 score compared to the other two models.These results underscore the effectiveness of integrating RF and WOA into ResNet-18 for pavement crack detection applications.We applied the proposed approach to a dataset of pavement images,achieving an accuracy of 97.16%and an AUC of 0.984.Our results demonstrate that the proposed approach surpasses existing methods for pavement crack detection,offering a promising solution for the automatic identification of pavement cracks.By leveraging this approach,potential safety hazards can be identified more effectively,enabling timely repairs and maintenance measures.Lastly,the findings of this study also emphasize the potential of integrating RF and WOA with deep learning for pavement crack detection,providing road authorities with the necessary tools to make informed decisions regarding road infrastructure maintenance.展开更多
The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algo...The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems.展开更多
In the frame of our long-term study of cetacean abundance and distribution in polar marine ecosystems begun in1979,a drastic increase in the bowhead Balaena mysticetus North Atlantic "stock" was observed fro...In the frame of our long-term study of cetacean abundance and distribution in polar marine ecosystems begun in1979,a drastic increase in the bowhead Balaena mysticetus North Atlantic "stock" was observed from 2005 on,by a factor 30 and more:from 0.0002 per count between 1979 and 2003(one individual,n=5430 counts) to 0.06 per count from 2005 to 2014(34 individuals,n=6000 counts);the most significant part of the increase occurred from 2007 on.Other large whale species(Mysticeti) showed a similar pattern,mainly blue Balaenoptera musculus,humpback Megaptera novaeangliae and fin whales Balaenoptera physalus.This large and abrupt increase cannot logically be due to population growth,nor to survival of a hidden "relic" population,nor to a changing geographical distribution within the European Arctic,taking into account the importance of the coverage during this study.Our interpretation is that individuals passed through the Northwest and/or Northeast Passages from the larger Pacific stock into the almost depleted North Atlantic populations coinciding with a period of very low ice coverage—at the time the lowest ever recorded.In contrast,no clear evolution was detected neither for sperm whale Physeter macrocephalus nor for Minke whale Balaenoptera acusrostrata.展开更多
Although four species of odontocete and four species of baleen whale have been recorded in Prydz Bay,their vocalizations have been rarely investigated.Underwater vocalizations were recorded during March 2017 in Prydz ...Although four species of odontocete and four species of baleen whale have been recorded in Prydz Bay,their vocalizations have been rarely investigated.Underwater vocalizations were recorded during March 2017 in Prydz Bay,Antarctica.Bio-duck sounds,downsweeps,inverted“u”shape signals,whistles,pulsed sounds,and broadband clicks were recorded.Bio-duck sounds and downsweeps were associated with Antarctic minke whales(Balaenoptera bonaerensis)based on visual observations.Similarities between inverted“u”shape signals,biphonic calls,and clicks with vocalizations previously described for killer whales(Orcinus orca)lead us believe the presence of Antarctic killer whales.According to sound structures,signal characteristics,and recording location,Antarctic type C killer whales were the most probable candidates to produce these detected calls.These represent the fi rst detection of inverted“u”shape signals in Antarctic waters,and the fi rst report of Antarctic killer whale in Prydz Bay based on passive acoustic monitoring.The co-existence of Antarctic minke and killer whales may imply that minke whales can detect diff erences between the sounds of mammal-eating and fi sh-eating killer whales.Our descriptions of these underwater vocalizations contribute to the limited body of information regarding the distribution and acoustic behavior of cetaceans in Prydz Bay.展开更多
Identifying home ranges—those areas traversed by individuals in their normal foraging,mating,and parenting activities—is an important aspect of cetacean study.Understanding these ranges facilitates identification of...Identifying home ranges—those areas traversed by individuals in their normal foraging,mating,and parenting activities—is an important aspect of cetacean study.Understanding these ranges facilitates identification of resource use and conservation.Fin and humpback whales occur in Antarctica during the austral summer,but information regarding their home ranges is limited.Using opportunistically collected whale sighting data from eight consecutive summer seasons spanning 2010–2017,we approximate the home ranges of humpback and fin whales around Drake Passage(DRA),West of Antarctic Peninsula(WAP),South Shetland Islands(SSI),an area northwest of the Weddell Sea(WED),and around the South Orkney Islands(SOI).Approximate home ranges are identified using Kernel Density Estimation(KDE).Most fin whales occurred north and northwest of the SOI,which suggests that waters near these islands support concentrations of this species.Most humpback whales were observed around the SSI,but unlike fin whales,their distributions were highly variable in other areas.KDE suggests spatial segregation in areas where both species exist such as SOI,SSI,and WPA.Partial redundancy analysis(pRDA)suggests that the distributions of these species are more affected by spatial variables(latitude,longitude)than by local scale variables such as sea surface temperature and depth.This study presents a visual approximation of the home ranges of fin and humpback whales,and identifies variation in the effects of space and environmental variables on the distributions of these whales at different spatial scales.展开更多
The unstructured growth of abnormal cells in the lung tissue creates tumor.The early detection of lung tumor helps the patients avoiding the death rate and gives better treatment.Various medical image modalities can h...The unstructured growth of abnormal cells in the lung tissue creates tumor.The early detection of lung tumor helps the patients avoiding the death rate and gives better treatment.Various medical image modalities can help the physicians in the diagnosis of disease.Many research works have been proposed for the early detection of lung tumor.High computation time and misidentification of tumor are the prevailing issues.In order to overcome these issues,this paper has proposed a hybrid classifier of Atrous Spatial Pyramid Pooling(ASPP)-Unet architecture withWhale Optimization Algorithm(ASPP-Unet-WOA).To get a fine tuning detection of tumor in the Computed Tomography(CT)of lung image,this model needs pre-processing using Gabor filter.Secondly,feature segmentation is done using Guaranteed Convergence Particle Swarm Optimization.Thirdly,feature selection is done using Binary Grasshopper Optimization Algorithm.This proposed(ASPPUnet-WOA)is implemented in the dataset of National Cancer Institute(NCI)Lung Cancer Database Consortium.Various performance metric measures are evaluated and compared to the existing classifiers.The accuracy of Deep Convolutional Neural Network(DCNN)is 93.45%,Convolutional Neural Network(CNN)is 91.67%,UNet obtains 95.75%and ASPP-UNet-WOA obtains 98.68%.compared to the other techniques.展开更多
Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in term...Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in terms of simple calculation and high computational accuracy,but its convergence speed is slow and it is easy to fall into the local optimal solution.In order to overcome the shortcomings,this paper integrates adaptive neighborhood and hybrid mutation strategies into whale optimization algorithms,designs the average distance from itself to other whales as an adaptive neighborhood radius,and chooses to learn from the optimal solution in the neighborhood instead of random learning strategies.The hybrid mutation strategy is used to enhance the ability of algorithm to jump out of the local optimal solution.A new whale optimization algorithm(HMNWOA)is proposed.The proposed algorithm inherits the global search capability of the original algorithm,enhances the exploitation ability,improves the quality of the population,and thus improves the convergence speed of the algorithm.A feature selection algorithm based on binary HMNWOA is proposed.Twelve standard datasets from UCI repository test the validity of the proposed algorithm for feature selection.The experimental results show that HMNWOA is very competitive compared to the other six popular feature selection methods in improving the classification accuracy and reducing the number of features,and ensures that HMNWOA has strong search ability in the search feature space.展开更多
The Hawaiian Islands, and particularly the Maui 4-island region, are a critical breeding and calving habitat for humpback whales (Megaptera novaeangliae) belonging to the Hawaii distinct population segment. Our aims w...The Hawaiian Islands, and particularly the Maui 4-island region, are a critical breeding and calving habitat for humpback whales (Megaptera novaeangliae) belonging to the Hawaii distinct population segment. Our aims were to test the use of platforms-of-opportunity to determine trends in mother-calf pod use of the region and to present opportunistic platforms as an alternative method of long-term, cross-seasonal monitoring. Data were collected from whale watching vessels over a 4-year period and analyzed using occupancy models to determine the probability of habitat use of pods with calves and pods without calves within the study area. Detection probability was influenced by survey effort and month for all pod types with detection of adult only pods further influenced by year. Pods with a calf showed a preference for shallow (<100 meters) low latitude waters (<20.7°N), while pods without a calf preferred deeper waters (>75 meters). Results presented here align with previous work, both in Hawaii and in other breeding grounds, which show a distinct segregation of mothers with a calf from other age-classes of humpback whales. The need for long-term continuous monitoring of cetacean populations is crucial to ensure species conservation. Data collected aboard platforms-of-opportunity, as presented here, provide important insight on humpback whale spatial and temporal distribution, which are essential for species protection and management.展开更多
The automatic generation of test data is a key step in realizing automated testing.Most automated testing tools for unit testing only provide test case execution drivers and cannot generate test data that meets covera...The automatic generation of test data is a key step in realizing automated testing.Most automated testing tools for unit testing only provide test case execution drivers and cannot generate test data that meets coverage requirements.This paper presents an improved Whale Genetic Algorithm for generating test data re-quired for unit testing MC/DC coverage.The proposed algorithm introduces an elite retention strategy to avoid the genetic algorithm from falling into iterative degradation.At the same time,the mutation threshold of the whale algorithm is introduced to balance the global exploration and local search capabilities of the genetic al-gorithm.The threshold is dynamically adjusted according to the diversity and evolution stage of current popu-lation,which positively guides the evolution of the population.Finally,an improved crossover strategy is pro-posed to accelerate the convergence of the algorithm.The improved whale genetic algorithm is compared with genetic algorithm,whale algorithm and particle swarm algorithm on two benchmark programs.The results show that the proposed algorithm is faster for test data generation than comparison methods and can provide better coverage with fewer evaluations,and has great advantages in generating test data.展开更多
Whale age at sexual maturity is one of the most important biological parameters that can be used in stock management and population analysis. Earplugs have been widely used as an indicator of age among rorquals. It ha...Whale age at sexual maturity is one of the most important biological parameters that can be used in stock management and population analysis. Earplugs have been widely used as an indicator of age among rorquals. It has also been accepted that the transition phase in the earplug can be used as an indicator of age at sexual maturity in fin whales, sei whales, and Antarctic minke whales. This study aimed to provide further insight into the utility of the transition phase as an indicator of age at sexual maturity in the North Pacific common minke whales, which has not yet been clarified. The relationship between sexual maturity and transition phase in earplugs was examined using 981 readable earplugs from common minke whales that were sampled at the JARPN and JARPN II scientific permit survey platform in the western North Pacific from 1994 to 2011. The transition phase was recognized in 53.2% of mature males and in 58.6% of mature females. Most whales in which the transition phase was recognized in the earplug were sexually mature. A significant correlation was found between the number of corpora and time after sexual maturation, as revealed by the transition phase, demonstrating that the transition phase is a valid indicator of age at sexual maturity in common minke whales. However, it was difficult to recognize the transition phase in whales that had recently attained sexual maturity because insufficient time had elapsed since its formation. To avoid potential bias, the use of earplugs as an indicator of age should be restricted to whales more than 12 years old.展开更多
Developmental changes in the body proportions of western North Pacific Bryde’s whales (Balaenoptera edeni) were investigated by examining the proportion of each body part to the total body length. The head and chest ...Developmental changes in the body proportions of western North Pacific Bryde’s whales (Balaenoptera edeni) were investigated by examining the proportion of each body part to the total body length. The head and chest region increased to a certain body length;subsequently, the length of head region stabilized, and that of the chest region decreased. The length of the abdominal region remained constant to a certain body length and subsequently showed a marked increase, and that of the tail region decreased consistently. The length of dorsal fin and flukes decreased consistently, whereas that of the flippers remained constant to a certain body length and subsequently decreased. The relative growth pattern determined by an allometric analysis was positive for the head region but negative for the lower body part, flippers, flukes, and dorsal fin. Both sexes demonstrated the same growth pattern, but the coefficients differed. This is the first study to investigate developmental changes in the body proportions of Bryde’s whales using more than 700 specimens covering a wide body length range. We believe that the results of this study will contribute to various research fields, including taxonomy, phylogeny, and feeding ecology of this species.展开更多
The coast of Japan is a migratory corridor for the western stock of the gray whales (Eschrichtius robustus), which was once considered as extinct and remains endangered. According to the historical records, from 1955 ...The coast of Japan is a migratory corridor for the western stock of the gray whales (Eschrichtius robustus), which was once considered as extinct and remains endangered. According to the historical records, from 1955 to 2014, only 21 gray whales occurrence has been recorded in 59 years. However, from 2015 to 2016, intensive occurrence including the seven sightings and the two strandings were noted. In this paper, we found that those sightings were re-sightings of the same individual, which was initially sighted off Sakhalin during August, 2014. On 4 March, 2016, a young female gray whale (8.9 m in body length) was stranded at Wadaura beach, Chiba prefecture. We also conducted research on this animal including taking pictures and external measurements. In addition, we flensed this animal to observe the internal organs and collect a skeletal specimen. The reason for the death of this animal remains unclear;however, from its external characteristics, we identified that this animal was not an identical one, sighted off Sakhalin and the coast of Japan from 2014 to 2016. On 5 April, 2016, another young female gray whale (7 m in body length) was stranded at Arai beach, Shizuoka prefecture. We concluded that from 2015 to 2016, at least three distinct gray whales have migrated along the coast of Japan.展开更多
There is a growing interest in the study development of artificial intelligence and machine learning,especially regarding the support vector machine pattern classification method.This study proposes an enhanced implem...There is a growing interest in the study development of artificial intelligence and machine learning,especially regarding the support vector machine pattern classification method.This study proposes an enhanced implementation of the well-known whale optimisation algorithm,which combines chaotic and opposition-based learning strategies,which is adopted for hyper-parameter optimisation and feature selection machine learning challenges.The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation.The Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted for simultaneous feature selection and support vector machine hyper-parameter tuning and validated for medical diagnostics by using breast cancer,diabetes,and erythemato-squamous dataset.The performance of the proposed model is compared with multiple competitive support vector machine models boosted with other metaheuristics,including another improved whale optimisation approach,particle swarm optimisation algorithm,bacterial foraging optimisation algorithms,and genetic algorithms.Results of the simulation show that the proposed model outperforms other competitors concerning the performance of classification and the selected subset feature size.展开更多
基金the VNUHCM-University of Information Technology’s Scientific Research Support Fund.
文摘Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.
文摘Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).
基金the National Natural Science Foundation of China(No.62176146)。
文摘A multi-strategy hybrid whale optimization algorithm(MSHWOA)for complex constrained optimization problems is proposed to overcome the drawbacks of easily trapping into local optimum,slow convergence speed and low optimization precision.Firstly,the population is initialized by introducing the theory of good point set,which increases the randomness and diversity of the population and lays the foundation for the global optimization of the algorithm.Then,a novel linearly update equation of convergence factor is designed to coordinate the abilities of exploration and exploitation.At the same time,the global exploration and local exploitation capabilities are improved through the siege mechanism of Harris Hawks optimization algorithm.Finally,the simulation experiments are conducted on the 6 benchmark functions and Wilcoxon rank sum test to evaluate the optimization performance of the improved algorithm.The experimental results show that the proposed algorithm has more significant improvement in optimization accuracy,convergence speed and robustness than the comparison algorithm.
基金This research is part of a project funded by Imam Abdulrahman Bin Faisal University,under Grant Number 2020-083-BASRC.
文摘Various feature selection algorithms are usually employed to improve classification models’overall performance.Optimization algorithms typically accompany such algorithms to select the optimal set of features.Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics.The present paper presents two Stages of Local Search models for feature selection based on WOA(Whale Optimization Algorithm)and Great Deluge(GD).GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search.Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm.In addition,disruptive selection(DS)is employed to select the solutions from the population for local search.DS is chosen to maintain the diversity of the population via enhancing low and high-quality solutions.Fifteen(15)standard benchmark datasets provided by the University of California Irvine(UCI)repository were used in evaluating the proposed approaches’performance.Next,a comparison was made with four population-based algorithms as wrapper feature selection methods from the literature.The proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper methods.Hence,the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.
文摘Image processing,agricultural production,andfield monitoring are essential studies in the researchfield.Plant diseases have an impact on agricultural production and quality.Agricultural disease detection at a preliminary phase reduces economic losses and improves the quality of crops.Manually identifying the agricultural pests is usually evident in plants;also,it takes more time and is an expensive technique.A drone system has been developed to gather photographs over enormous regions such as farm areas and plantations.An atmosphere generates vast amounts of data as it is monitored closely;the evaluation of this big data would increase the production of agricultural production.This paper aims to identify pests in mango trees such as hoppers,mealybugs,inflorescence midges,fruitflies,and stem borers.Because of the massive volumes of large-scale high-dimensional big data collected,it is necessary to reduce the dimensionality of the input for classify-ing images.The community-based cumulative algorithm was used to classify the pests in the existing system.The proposed method uses the Entropy-ELM method with Whale Optimization to improve the classification in detecting pests in agricul-ture.The Entropy-ELM method with the Whale Optimization Algorithm(WOA)is used for feature selection,enhancing mango pests’classification accuracy.Support Vector Machines(SVMs)are especially effective for classifying while users get var-ious classes in which they are interested.They are created as suitable classifiers to categorize any dataset in Big Data effectively.The proposed Entropy-ELM-WOA is more capable compared to the existing systems.
基金supported by the Technical Basic Scientific Research Projects of State Administration of Science,Technology and Industry for National Defence,PRC (Grant No.JSZL2019204C001).
文摘In this work,an improved active kriging method based on the AK-IS and truncated importance sampling(TIS)method is proposed to efficiently evaluate structural reliability.The novel method called AWK-TIS is inspired by AK-IS and RBF-GA previously published in the literature.The innovation of the AWK-TIS is that TIS is adopted to lessen the sample pool size significantly,and the whale optimization algorithm(WOA)is employed to acquire the optimal Krigingmodel and themost probable point(MPP).To verify the performance of theAWK-TISmethod for structural reliability,four numerical cases which are utilized as benchmarks in literature and one real engineering problem about a jet van manipulate mechanism are tested.The results indicate the accuracy and efficiency of the proposed method.
基金supported by Anhui Polytechnic University Introduced Talents Research Fund(No.2021YQQ064)Anhui Polytechnic University ScientificResearch Project(No.Xjky2022168).
文摘Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems.However,with the increased dimensions,higher requirements are put forward for algorithm performance.The double population whale optimization algorithm with distributed collaboration and reverse learning ability(DCRWOA)is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems.In the DCRWOA algorithm,the novel double population search strategy is constructed.Meanwhile,the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search area.Numerical experi-ments are carried out using standard test functions with different dimensions(10,50,100,200).The optimization case of shield construction parameters is also used to test the practical application performance of the proposed algo-rithm.The results show that the DCRWOA algorithm has higher optimization accuracy and stability,and the convergence speed is significantly improved.Therefore,the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.
文摘Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses.Recent advancements in deep learning(DL)techniques have shown promising results in detecting pavement cracks;however,the selection of relevant features for classification remains challenging.In this study,we propose a new approach for pavement crack detection that integrates deep learning for feature extraction,the whale optimization algorithm(WOA)for feature selection,and random forest(RF)for classification.The performance of the models was evaluated using accuracy,recall,precision,F1 score,and area under the receiver operating characteristic curve(AUC).Our findings reveal that Model 2,which incorporates RF into the ResNet-18 architecture,outperforms baseline Model 1 across all evaluation metrics.Nevertheless,our proposed model,which combines ResNet-18 with both WOA and RF,achieves significantly higher accuracy,recall,precision,and F1 score compared to the other two models.These results underscore the effectiveness of integrating RF and WOA into ResNet-18 for pavement crack detection applications.We applied the proposed approach to a dataset of pavement images,achieving an accuracy of 97.16%and an AUC of 0.984.Our results demonstrate that the proposed approach surpasses existing methods for pavement crack detection,offering a promising solution for the automatic identification of pavement cracks.By leveraging this approach,potential safety hazards can be identified more effectively,enabling timely repairs and maintenance measures.Lastly,the findings of this study also emphasize the potential of integrating RF and WOA with deep learning for pavement crack detection,providing road authorities with the necessary tools to make informed decisions regarding road infrastructure maintenance.
基金The National Natural Science Foundation of China(Grant No.81973791)funded this research.
文摘The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems.
文摘In the frame of our long-term study of cetacean abundance and distribution in polar marine ecosystems begun in1979,a drastic increase in the bowhead Balaena mysticetus North Atlantic "stock" was observed from 2005 on,by a factor 30 and more:from 0.0002 per count between 1979 and 2003(one individual,n=5430 counts) to 0.06 per count from 2005 to 2014(34 individuals,n=6000 counts);the most significant part of the increase occurred from 2007 on.Other large whale species(Mysticeti) showed a similar pattern,mainly blue Balaenoptera musculus,humpback Megaptera novaeangliae and fin whales Balaenoptera physalus.This large and abrupt increase cannot logically be due to population growth,nor to survival of a hidden "relic" population,nor to a changing geographical distribution within the European Arctic,taking into account the importance of the coverage during this study.Our interpretation is that individuals passed through the Northwest and/or Northeast Passages from the larger Pacific stock into the almost depleted North Atlantic populations coinciding with a period of very low ice coverage—at the time the lowest ever recorded.In contrast,no clear evolution was detected neither for sperm whale Physeter macrocephalus nor for Minke whale Balaenoptera acusrostrata.
基金Supported by the National Natural Science Foundation of China(No.41906170)the Indian Ocean Ninety-east Ridge Ecosystem and Marine Environment Monitoring and Protection(No.DY135-E2-4)+1 种基金the Cooperation of Top Predators Observation in the Southern Ocean(No.QT4519003)the China-ASEAN Maritime Cooperation Fund。
文摘Although four species of odontocete and four species of baleen whale have been recorded in Prydz Bay,their vocalizations have been rarely investigated.Underwater vocalizations were recorded during March 2017 in Prydz Bay,Antarctica.Bio-duck sounds,downsweeps,inverted“u”shape signals,whistles,pulsed sounds,and broadband clicks were recorded.Bio-duck sounds and downsweeps were associated with Antarctic minke whales(Balaenoptera bonaerensis)based on visual observations.Similarities between inverted“u”shape signals,biphonic calls,and clicks with vocalizations previously described for killer whales(Orcinus orca)lead us believe the presence of Antarctic killer whales.According to sound structures,signal characteristics,and recording location,Antarctic type C killer whales were the most probable candidates to produce these detected calls.These represent the fi rst detection of inverted“u”shape signals in Antarctic waters,and the fi rst report of Antarctic killer whale in Prydz Bay based on passive acoustic monitoring.The co-existence of Antarctic minke and killer whales may imply that minke whales can detect diff erences between the sounds of mammal-eating and fi sh-eating killer whales.Our descriptions of these underwater vocalizations contribute to the limited body of information regarding the distribution and acoustic behavior of cetaceans in Prydz Bay.
基金This work was conducted with logistical and financial support of the Instituto Antártico Argentino.
文摘Identifying home ranges—those areas traversed by individuals in their normal foraging,mating,and parenting activities—is an important aspect of cetacean study.Understanding these ranges facilitates identification of resource use and conservation.Fin and humpback whales occur in Antarctica during the austral summer,but information regarding their home ranges is limited.Using opportunistically collected whale sighting data from eight consecutive summer seasons spanning 2010–2017,we approximate the home ranges of humpback and fin whales around Drake Passage(DRA),West of Antarctic Peninsula(WAP),South Shetland Islands(SSI),an area northwest of the Weddell Sea(WED),and around the South Orkney Islands(SOI).Approximate home ranges are identified using Kernel Density Estimation(KDE).Most fin whales occurred north and northwest of the SOI,which suggests that waters near these islands support concentrations of this species.Most humpback whales were observed around the SSI,but unlike fin whales,their distributions were highly variable in other areas.KDE suggests spatial segregation in areas where both species exist such as SOI,SSI,and WPA.Partial redundancy analysis(pRDA)suggests that the distributions of these species are more affected by spatial variables(latitude,longitude)than by local scale variables such as sea surface temperature and depth.This study presents a visual approximation of the home ranges of fin and humpback whales,and identifies variation in the effects of space and environmental variables on the distributions of these whales at different spatial scales.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(GRP/303/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R203),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The unstructured growth of abnormal cells in the lung tissue creates tumor.The early detection of lung tumor helps the patients avoiding the death rate and gives better treatment.Various medical image modalities can help the physicians in the diagnosis of disease.Many research works have been proposed for the early detection of lung tumor.High computation time and misidentification of tumor are the prevailing issues.In order to overcome these issues,this paper has proposed a hybrid classifier of Atrous Spatial Pyramid Pooling(ASPP)-Unet architecture withWhale Optimization Algorithm(ASPP-Unet-WOA).To get a fine tuning detection of tumor in the Computed Tomography(CT)of lung image,this model needs pre-processing using Gabor filter.Secondly,feature segmentation is done using Guaranteed Convergence Particle Swarm Optimization.Thirdly,feature selection is done using Binary Grasshopper Optimization Algorithm.This proposed(ASPPUnet-WOA)is implemented in the dataset of National Cancer Institute(NCI)Lung Cancer Database Consortium.Various performance metric measures are evaluated and compared to the existing classifiers.The accuracy of Deep Convolutional Neural Network(DCNN)is 93.45%,Convolutional Neural Network(CNN)is 91.67%,UNet obtains 95.75%and ASPP-UNet-WOA obtains 98.68%.compared to the other techniques.
基金This work was supported by the National Natural Science Foundation of China(Grant No.2017YFC0403605 and No.11601419).
文摘Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in terms of simple calculation and high computational accuracy,but its convergence speed is slow and it is easy to fall into the local optimal solution.In order to overcome the shortcomings,this paper integrates adaptive neighborhood and hybrid mutation strategies into whale optimization algorithms,designs the average distance from itself to other whales as an adaptive neighborhood radius,and chooses to learn from the optimal solution in the neighborhood instead of random learning strategies.The hybrid mutation strategy is used to enhance the ability of algorithm to jump out of the local optimal solution.A new whale optimization algorithm(HMNWOA)is proposed.The proposed algorithm inherits the global search capability of the original algorithm,enhances the exploitation ability,improves the quality of the population,and thus improves the convergence speed of the algorithm.A feature selection algorithm based on binary HMNWOA is proposed.Twelve standard datasets from UCI repository test the validity of the proposed algorithm for feature selection.The experimental results show that HMNWOA is very competitive compared to the other six popular feature selection methods in improving the classification accuracy and reducing the number of features,and ensures that HMNWOA has strong search ability in the search feature space.
文摘The Hawaiian Islands, and particularly the Maui 4-island region, are a critical breeding and calving habitat for humpback whales (Megaptera novaeangliae) belonging to the Hawaii distinct population segment. Our aims were to test the use of platforms-of-opportunity to determine trends in mother-calf pod use of the region and to present opportunistic platforms as an alternative method of long-term, cross-seasonal monitoring. Data were collected from whale watching vessels over a 4-year period and analyzed using occupancy models to determine the probability of habitat use of pods with calves and pods without calves within the study area. Detection probability was influenced by survey effort and month for all pod types with detection of adult only pods further influenced by year. Pods with a calf showed a preference for shallow (<100 meters) low latitude waters (<20.7°N), while pods without a calf preferred deeper waters (>75 meters). Results presented here align with previous work, both in Hawaii and in other breeding grounds, which show a distinct segregation of mothers with a calf from other age-classes of humpback whales. The need for long-term continuous monitoring of cetacean populations is crucial to ensure species conservation. Data collected aboard platforms-of-opportunity, as presented here, provide important insight on humpback whale spatial and temporal distribution, which are essential for species protection and management.
文摘The automatic generation of test data is a key step in realizing automated testing.Most automated testing tools for unit testing only provide test case execution drivers and cannot generate test data that meets coverage requirements.This paper presents an improved Whale Genetic Algorithm for generating test data re-quired for unit testing MC/DC coverage.The proposed algorithm introduces an elite retention strategy to avoid the genetic algorithm from falling into iterative degradation.At the same time,the mutation threshold of the whale algorithm is introduced to balance the global exploration and local search capabilities of the genetic al-gorithm.The threshold is dynamically adjusted according to the diversity and evolution stage of current popu-lation,which positively guides the evolution of the population.Finally,an improved crossover strategy is pro-posed to accelerate the convergence of the algorithm.The improved whale genetic algorithm is compared with genetic algorithm,whale algorithm and particle swarm algorithm on two benchmark programs.The results show that the proposed algorithm is faster for test data generation than comparison methods and can provide better coverage with fewer evaluations,and has great advantages in generating test data.
文摘Whale age at sexual maturity is one of the most important biological parameters that can be used in stock management and population analysis. Earplugs have been widely used as an indicator of age among rorquals. It has also been accepted that the transition phase in the earplug can be used as an indicator of age at sexual maturity in fin whales, sei whales, and Antarctic minke whales. This study aimed to provide further insight into the utility of the transition phase as an indicator of age at sexual maturity in the North Pacific common minke whales, which has not yet been clarified. The relationship between sexual maturity and transition phase in earplugs was examined using 981 readable earplugs from common minke whales that were sampled at the JARPN and JARPN II scientific permit survey platform in the western North Pacific from 1994 to 2011. The transition phase was recognized in 53.2% of mature males and in 58.6% of mature females. Most whales in which the transition phase was recognized in the earplug were sexually mature. A significant correlation was found between the number of corpora and time after sexual maturation, as revealed by the transition phase, demonstrating that the transition phase is a valid indicator of age at sexual maturity in common minke whales. However, it was difficult to recognize the transition phase in whales that had recently attained sexual maturity because insufficient time had elapsed since its formation. To avoid potential bias, the use of earplugs as an indicator of age should be restricted to whales more than 12 years old.
文摘Developmental changes in the body proportions of western North Pacific Bryde’s whales (Balaenoptera edeni) were investigated by examining the proportion of each body part to the total body length. The head and chest region increased to a certain body length;subsequently, the length of head region stabilized, and that of the chest region decreased. The length of the abdominal region remained constant to a certain body length and subsequently showed a marked increase, and that of the tail region decreased consistently. The length of dorsal fin and flukes decreased consistently, whereas that of the flippers remained constant to a certain body length and subsequently decreased. The relative growth pattern determined by an allometric analysis was positive for the head region but negative for the lower body part, flippers, flukes, and dorsal fin. Both sexes demonstrated the same growth pattern, but the coefficients differed. This is the first study to investigate developmental changes in the body proportions of Bryde’s whales using more than 700 specimens covering a wide body length range. We believe that the results of this study will contribute to various research fields, including taxonomy, phylogeny, and feeding ecology of this species.
文摘The coast of Japan is a migratory corridor for the western stock of the gray whales (Eschrichtius robustus), which was once considered as extinct and remains endangered. According to the historical records, from 1955 to 2014, only 21 gray whales occurrence has been recorded in 59 years. However, from 2015 to 2016, intensive occurrence including the seven sightings and the two strandings were noted. In this paper, we found that those sightings were re-sightings of the same individual, which was initially sighted off Sakhalin during August, 2014. On 4 March, 2016, a young female gray whale (8.9 m in body length) was stranded at Wadaura beach, Chiba prefecture. We also conducted research on this animal including taking pictures and external measurements. In addition, we flensed this animal to observe the internal organs and collect a skeletal specimen. The reason for the death of this animal remains unclear;however, from its external characteristics, we identified that this animal was not an identical one, sighted off Sakhalin and the coast of Japan from 2014 to 2016. On 5 April, 2016, another young female gray whale (7 m in body length) was stranded at Arai beach, Shizuoka prefecture. We concluded that from 2015 to 2016, at least three distinct gray whales have migrated along the coast of Japan.
文摘There is a growing interest in the study development of artificial intelligence and machine learning,especially regarding the support vector machine pattern classification method.This study proposes an enhanced implementation of the well-known whale optimisation algorithm,which combines chaotic and opposition-based learning strategies,which is adopted for hyper-parameter optimisation and feature selection machine learning challenges.The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation.The Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted for simultaneous feature selection and support vector machine hyper-parameter tuning and validated for medical diagnostics by using breast cancer,diabetes,and erythemato-squamous dataset.The performance of the proposed model is compared with multiple competitive support vector machine models boosted with other metaheuristics,including another improved whale optimisation approach,particle swarm optimisation algorithm,bacterial foraging optimisation algorithms,and genetic algorithms.Results of the simulation show that the proposed model outperforms other competitors concerning the performance of classification and the selected subset feature size.
