Electric-heat coupling characteristics of a cogeneration system and the operating mode of fixing electricity with heat are the main reasons for wind abandonment during the heating season in the Three North area.To imp...Electric-heat coupling characteristics of a cogeneration system and the operating mode of fixing electricity with heat are the main reasons for wind abandonment during the heating season in the Three North area.To improve the wind-power absorption capacity and operating economy of the system,the structure of the system is improved by adding a heat storage device and an electric boiler.First,aiming at the minimum operating cost of the system,the optimal scheduling model of the cogeneration system,including a heat storage device and electric boiler,is constructed.Second,according to the characteristics of the problem,a cultural gene algorithm program is compiled to simulate the calculation example.Finally,through the system improvement,the comparison between the conditions before and after and the simulation solutions of similar algorithms prove the effectiveness of the proposed scheme.The simulation results show that adding the heat storage device and electric boiler to the scheduling optimization process not only improves the wind power consumption capacity of the cogeneration system but also reduces the operating cost of the system by significantly reducing the coal consumption of the unit and improving the economy of the system operation.The cultural gene algorithm framework has both the global evolution process of the population and the local search for the characteristics of the problem,which has a better optimization effect on the solution.展开更多
This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image datasets.Since binarization of the input ...This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image datasets.Since binarization of the input image is the most important and difficult step in the detection of VPLN,a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects(CCO)and hence enriches the solution space with more solution candidates.Due to the combination of the outputs of the three binarization techniques,many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate solutions.The pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate prototype.Using any of the previous versions will give moderate results but with very low speed.Hence,a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate symbols.The memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used techniques.Also,a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar patterns.Various image samples with a wide range of scale,orientation,and illumination conditions have been experimented with to verify the effect of the new improvements.Encouraging results with 97.55%detection precision have been reported using the recent challenging public Chinese City Parking Dataset(CCPD)outperforming the author of the dataset by 3.05%and the state-of-the-art technique by 1.45%.展开更多
Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-de...Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due time.It is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production systems.Two objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the problem.To obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy algorithm.The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers.Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.展开更多
Inter-satellite link(ISL)scheduling is required by the BeiDou Navigation Satellite System(BDS)to guarantee the system ranging and communication performance.In the BDS,a great number of ISL scheduling instances must be...Inter-satellite link(ISL)scheduling is required by the BeiDou Navigation Satellite System(BDS)to guarantee the system ranging and communication performance.In the BDS,a great number of ISL scheduling instances must be addressed every day,which will certainly spend a lot of time via normal metaheuristics and hardly meet the quick-response requirements that often occur in real-world applications.To address the dual requirements of normal and quick-response ISL schedulings,a data-driven heuristic assisted memetic algorithm(DHMA)is proposed in this paper,which includes a high-performance memetic algorithm(MA)and a data-driven heuristic.In normal situations,the high-performance MA that hybridizes parallelism,competition,and evolution strategies is performed for high-quality ISL scheduling solutions over time.When in quick-response situations,the data-driven heuristic is performed to quickly schedule high-probability ISLs according to a prediction model,which is trained from the high-quality MA solutions.The main idea of the DHMA is to address normal and quick-response schedulings separately,while high-quality normal scheduling data are trained for quick-response use.In addition,this paper also presents an easy-to-understand ISL scheduling model and its NP-completeness.A seven-day experimental study with 10080 one-minute ISL scheduling instances shows the efficient performance of the DHMA in addressing the ISL scheduling in normal(in 84 hours)and quick-response(in 0.62 hour)situations,which can well meet the dual scheduling requirements in real-world BDS applications.展开更多
A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared...A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.展开更多
Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance o...Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance of Gaussian process model.However,the common-used algorithm has the disadvantages of difficult determination of iteration steps,over-dependence of optimization effect on initial values,and easily falling into local optimum.To solve this problem,a method combining the Gaussian process with memetic algorithm was proposed.Based on this method,memetic algorithm was used to search the optimal hyper parameters of Gaussian process regression(GPR)model in the training process and form MA-GPR algorithms,and then the model was used to predict and test the results.When used in the marine long-range precision strike system(LPSS)battle effectiveness evaluation,the proposed MA-GPR model significantly improved the prediction accuracy,compared with the conjugate gradient method and the genetic algorithm optimization process.展开更多
A memetic algorithm （MA） for a multi-mode resourceconstrained project scheduling problem （MRCPSP） is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The f...A memetic algorithm （MA） for a multi-mode resourceconstrained project scheduling problem （MRCPSP） is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The fitness function makes use of a mechanism called ＂strategic oscillation＂ to make the search process have a higher probability to visit solutions around a ＂feasible boundary＂. One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A detailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.展开更多
This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time(EADHFSP-ST)that simultaneously optimi...This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time(EADHFSP-ST)that simultaneously optimizes the makespan and the energy consumption.We develop a mixed integer linear programming model to describe this problem and present a two-stage adaptive memetic algorithm(TAMA)with a surprisingly popular mechanism.First,a hybrid initialization strategy is designed based on the two optimization objectives to ensure the convergence and diversity of solutions.Second,multiple population co-evolutionary approaches are proposed for global search to escape from traditional cross-randomization and to balance exploration and exploitation.Third,considering that the memetic algorithm(MA)framework is less efficient due to the randomness in the selection of local search operators,TAMA is proposed to balance the local and global searches.The first stage accumulates more experience for updating the surprisingly popular algorithm(SPA)model to guide the second stage operator selection and ensures population convergence.The second stage gets rid of local optimization and designs an elite archive to ensure population diversity.Fourth,five problem-specific operators are designed,and non-critical path deceleration and right-shift strategies are designed for energy efficiency.Finally,to evaluate the performance of the proposed algorithm,multiple experiments are performed on a benchmark with 45 instances.The experimental results show that the proposed TAMA can solve the problem effectively.展开更多
The heightened autonomy and robust adaptability inherent in a multi-robot system have proven pivotal in disaster search and rescue,agricultural irrigation,and environmental monitoring.This study addresses the coordina...The heightened autonomy and robust adaptability inherent in a multi-robot system have proven pivotal in disaster search and rescue,agricultural irrigation,and environmental monitoring.This study addresses the coordination of multiple robots for the surveillance of various key target positions within an area.This involves the allocation of target positions among robots and the concurrent planning of routes for each robot.To tackle these challenges,we formulate a unified optimization model addressing both target allocation and route planning.Subsequently,we introduce an adaptive memetic algorithm featuring dual-level local search strategies.This algorithm operates independently among and within robots to effectively solve the optimization problem associated with surveillance.The proposed method’s efficacy is substantiated through comparative numerical experiments and simulated experiments involving diverse scales of robot teams and different target positions.展开更多
Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network.This closed network is intended to share sensitive location-centric information from a source node ...Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network.This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms.The efficiency of the sensor node is energy bounded,acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors.Network management plays a significant role in wireless sensor networks,which was obsessed with the factors like the reliability of the network,resource management,energy-efficient routing,and scalability of services.The topology of the wireless sensor networks acts dri-ven factor for network efficiency which can be effectively maintained by perform-ing the clustering process effectively.More solutions and clustering algorithms have been offered by various researchers,but the concern of reduced efficiency in the routing process and network management still exists.This research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efficient clustering process in the wireless sensor networks.The memetic algorithm employs a local searching methodology to mitigate the premature convergence,while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain(i.e.,)best cluster head in the wireless sensor networks.The proposed hybrid algorithm is compared with the state of art clus-tering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time,energy consumption,throughput,etc.展开更多
This paper proposed a novel distributed memetic evolutionary model,where four modules distributed exploration,intensified exploitation,knowledge transfer,and evolutionary restart are coevolved to maximize their streng...This paper proposed a novel distributed memetic evolutionary model,where four modules distributed exploration,intensified exploitation,knowledge transfer,and evolutionary restart are coevolved to maximize their strengths and achieve superior global optimality.Distributed exploration evolves three independent populations by heterogenous operators.Intensified exploitation evolves an external elite archive in parallel with exploration to balance global and local searches.Knowledge transfer is based on a point-ring communication topology to share successful experiences among distinct search agents.Evolutionary restart adopts an adaptive perturbation strategy to control search diversity reasonably.Quantum computation is a newly emerging technique,which has powerful computing power and parallelized ability.Therefore,this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm,referred to as quantum-inspired distributed memetic algorithm(QDMA).In QDMA,individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum hyperspace.The QDMA integrates the superiorities of distributed,memetic,and quantum evolution.Computational experiments are carried out to evaluate the superior performance of QDMA.The results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum test.The superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model,but also to superior designs of each special component.展开更多
The problem of generating optimal paths for curvature-constrained unmanned aerial vehicles （UAVs） performing surveillance of multiple ground targets is addressed in this paper. UAVs are modeled as Dubins vehicles so...The problem of generating optimal paths for curvature-constrained unmanned aerial vehicles （UAVs） performing surveillance of multiple ground targets is addressed in this paper. UAVs are modeled as Dubins vehicles so that the constraints of UAVs＇ minimal turning radius can be taken into account. In view of the effective surveillance range of the sensors equipped on UAVs, the problem is formulated as a Dubins traveling salesman problem with neighborhood （DTSPN）. Considering its prohibitively high computational complexity, the Dubins paths in the sense of terminal heading relaxation are introduced to simplify the calculation of the Dubins distance, and a boundary-based encoding scheme is proposed to determine the visiting point of every target neighborhood. Then, an evolutionary algorithm is used to derive the optimal Dubins tour. To further enhance the quality of the solutions, a local search strategy based on approximate gradient is employed to improve the visiting points of target neighborhoods. Finally, by a minor modification to the individual encoding, the algorithm is easily extended to deal with other two more sophisticated DTSPN variants （multi-UAV scenario and multiple groups of targets scenario）. The performance of the algorithm is demonstrated through comparative experiments with other two state-of-the-art DTSPN algorithms identified in literature. Numerical simulations exhibit that the algorithm proposed in this paper can find high-quality solutions to the DTSPN with lower computational cost and produce significantly improved performance over the other algorithms.展开更多
Due to rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we sy...Due to rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we systematically explore the robustness of the Chinese air route network, and identify the vital edges which form the backbone of Chinese air transportation system.Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges, and hence the solution of this model is the set of vital edges. Counterintuitively,our results show that the most vital edges are not necessarily the edges of the highest topological importance, for which we provide an extensive explanation from the microscope view. Our findings also offer new insights to understanding and optimizing other real-world network systems.展开更多
Conflict avoidance （CA） plays a crucial role in guaranteeing the airspace safety. The cur- rent approaches, mostly focusing on a short-term situation which eliminates conflicts via local adjust- ment, cannot provide...Conflict avoidance （CA） plays a crucial role in guaranteeing the airspace safety. The cur- rent approaches, mostly focusing on a short-term situation which eliminates conflicts via local adjust- ment, cannot provide a global solution. Recently, long-term conflict avoidance approaches, which are proposed to provide solutions via strategically planning traffic flow from a global view, have attracted more attentions. With consideration of the situation in China, there are thousands of flights per day and the air route network is large and complex, which makes the long-term problem to be a large-scale combinatorial optimization problem with complex constraints. To minimize the risk of premature convergence being faced by current approaches and obtain higher quality solutions, in this work, we present an effective strategic framework based on a memetic algorithm （MA）, which can markedly improve search capability via a combination of population-based global search and local improve- ments made by individuals. In addition, a specially designed local search operator and an adaptive local search frequency strategy are proposed to improve the solution quality. Furthermore, a fast genetic algorithm （GA） is presented as the global optimization method. Empirical studies using real traffic data of the Chinese air route network and daily flight plans show that our approach outper- formed the existing approaches including the GA .based approach and the cooperative coevolution based approach as well as some well-known memetic algorithm based approaches.展开更多
The eigenface method that uses principal component analysis(PCA) has been the standard and popular method used in face recognition.This paper presents a PCA-memetic algorithm(PCA-MA) approach for feature selection.PCA...The eigenface method that uses principal component analysis(PCA) has been the standard and popular method used in face recognition.This paper presents a PCA-memetic algorithm(PCA-MA) approach for feature selection.PCA has been extended by MAs where the former was used for feature extraction/dimensionality reduction and the latter exploited for feature selection.Simulations were performed over ORL and YaleB face databases using Euclidean norm as the classifier.It was found that as far as the recognition rate is concerned,PCA-MA completely outperforms the eigenface method.We compared the performance of PCA extended with genetic algorithm(PCA-GA) with our proposed PCA-MA method.The results also clearly established the supremacy of the PCA-MA method over the PCA-GA method.We further extended linear discriminant analysis(LDA) and kernel principal component analysis(KPCA) approaches with the MA and observed significant improvement in recognition rate with fewer features.This paper also compares the performance of PCA-MA,LDA-MA and KPCA-MA approaches.展开更多
The use of unmanned aerial vehicles(UAVs) is becoming more commonplace in search-and-rescue tasks,but UAV search planning can be very complex due to limited response time, large search area, and multiple candidate sea...The use of unmanned aerial vehicles(UAVs) is becoming more commonplace in search-and-rescue tasks,but UAV search planning can be very complex due to limited response time, large search area, and multiple candidate search modes. In this paper, we present a UAV search planning problem where the search area is divided into a set of subareas and each subarea has a prior probability that the target is present in it. The problem aims to determine the search sequence of the subareas and the search mode for each subarea to maximize the probability of finding the target. We propose an adaptive memetic algorithm that combines a genetic algorithm with a set of local search procedures and dynamically determines which procedure to apply based on the past performance of the procedures measured in fitness improvement and diversity improvement during problem-solving. Computational experiments show that the proposed algorithm exhibits competitive performance compared to a set of state-of-the-art global search heuristics, non-adaptive memetic algorithms, and adaptive memetic algorithms on a wide set of problem instances.展开更多
This work presents a memetic Shuffled Frog Leaping Algorithm(SFLA)based tuning approach of an Integral Sliding Mode Controller(ISMC)for a quadrotor type of Unmanned Aerial Vehicles(UAV).Based on the Newton–Euler form...This work presents a memetic Shuffled Frog Leaping Algorithm(SFLA)based tuning approach of an Integral Sliding Mode Controller(ISMC)for a quadrotor type of Unmanned Aerial Vehicles(UAV).Based on the Newton–Euler formalism,a nonlinear dynamic model of the studied quadrotor is firstly established for control design purposes.Since the main parameters of the ISMC design are the gains of the sliding surfaces and signum functions of the switching control law,which are usually selected by repetitive and time-consuming trials-errors based procedures,a constrained optimization problem is formulated for the systematically tuning of these unknown variables.Under time-domain operating constraints,such an optimization-based tuning problem is effectively solved using the proposed SFLA metaheuristic with an empirical comparison to other evolutionary computation-and swarm intelligence-based algorithms such as the Crow Search Algorithm(CSA),Fractional Particle Swarm Optimization Memetic Algorithm(FPSOMA),Ant Bee Colony(ABC)and Harmony Search Algorithm(HSA).Numerical experiments are carried out for various sets of algorithms’parameters to achieve optimal gains of the sliding mode controllers for the altitude and attitude dynamics stabilization.Comparative studies revealed that the SFLA is a competitive and easily implemented algorithm with high performance in terms of robustness and non-premature convergence.Demonstrative results verified that the proposed metaheuristicsbased approach is a promising alternative for the systematic tuning of the effective design parameters in the integral sliding mode control framework.展开更多
Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development ...Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development of a novel memetic algorithm （MA） for neural network （NN） lcarnmg. Included in this is the integration of extremal optimization （EO） and Levenberg-Marquardt （LM） pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.展开更多
Purpose–The examination timetabling problem is an NP-hard problem.A large number of approaches for this problem are developed to find more appropriate search strategies.Hyper-heuristic is a kind of representative met...Purpose–The examination timetabling problem is an NP-hard problem.A large number of approaches for this problem are developed to find more appropriate search strategies.Hyper-heuristic is a kind of representative methods.In hyper-heuristic,the high-level search is executed to construct heuristic lists by traditional methods(such as Tabu search,variable neighborhoods and so on).The purpose of this paper is to apply the evolutionary strategy instead of traditional methods for high-level search to improve the capability of global search.Design/methodology/approach–This paper combines hyper-heuristic with evolutionary strategy to solve examination timetabling problems.First,four graph coloring heuristics are employed to construct heuristic lists.Within the evolutionary algorithm framework,the iterative initialization is utilized to improve the number of feasible solutions in the population;meanwhile,the crossover and mutation operators are applied to find potential heuristic lists in the heuristic space(high-level search).At last,two local search methods are combined to optimize the feasible solutions in the solution space(low-level search).Findings–Experimental results demonstrate that the proposed approach obtains competitive results and outperforms the compared approaches on some benchmark instances.Originality/value–The contribution of this paper is the development of a framework which combines evolutionary algorithm and hyper-heuristic for examination timetabling problems.展开更多
In the healthcare system,the Internet of Things(IoT)based distributed systems play a vital role in transferring the medical-related documents and information among the organizations to reduce the replication in medica...In the healthcare system,the Internet of Things(IoT)based distributed systems play a vital role in transferring the medical-related documents and information among the organizations to reduce the replication in medical tests.This datum is sensitive,and hence security is a must in transforming the sensational contents.In this paper,an Evolutionary Algorithm,namely the Memetic Algorithm is used for encrypting the text messages.The encrypted information is then inserted into the medical images using Discrete Wavelet Transform 1 level and 2 levels.The reverse method of the Memetic Algorithm is implemented when extracting a hidden message from the encoded letter.To show its precision,equivalent to five RGB images and five Grayscale images are used to test the proposed algorithm.The results of the proposed algorithm were analyzed using statistical methods,and the proposed algorithm showed the importance of data transfer in healthcare systems in a stable environment.In the future,to embed the privacy-preserving of medical data,it can be extended with blockchain technology.展开更多
基金supported by the National Natural Science Foundation of China(61773269)China Scholarship for Overseas Studying(CSC No.202008210181),Department of Education of Liaoning Province of China(LJKZ1110)+1 种基金the Natural Science Foundation of Liaoning Province of China(2019-KF-03-08)the Program for Shenyang High Level Innovative Talents(RC190042).
文摘Electric-heat coupling characteristics of a cogeneration system and the operating mode of fixing electricity with heat are the main reasons for wind abandonment during the heating season in the Three North area.To improve the wind-power absorption capacity and operating economy of the system,the structure of the system is improved by adding a heat storage device and an electric boiler.First,aiming at the minimum operating cost of the system,the optimal scheduling model of the cogeneration system,including a heat storage device and electric boiler,is constructed.Second,according to the characteristics of the problem,a cultural gene algorithm program is compiled to simulate the calculation example.Finally,through the system improvement,the comparison between the conditions before and after and the simulation solutions of similar algorithms prove the effectiveness of the proposed scheme.The simulation results show that adding the heat storage device and electric boiler to the scheduling optimization process not only improves the wind power consumption capacity of the cogeneration system but also reduces the operating cost of the system by significantly reducing the coal consumption of the unit and improving the economy of the system operation.The cultural gene algorithm framework has both the global evolution process of the population and the local search for the characteristics of the problem,which has a better optimization effect on the solution.
文摘This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image datasets.Since binarization of the input image is the most important and difficult step in the detection of VPLN,a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects(CCO)and hence enriches the solution space with more solution candidates.Due to the combination of the outputs of the three binarization techniques,many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate solutions.The pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate prototype.Using any of the previous versions will give moderate results but with very low speed.Hence,a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate symbols.The memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used techniques.Also,a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar patterns.Various image samples with a wide range of scale,orientation,and illumination conditions have been experimented with to verify the effect of the new improvements.Encouraging results with 97.55%detection precision have been reported using the recent challenging public Chinese City Parking Dataset(CCPD)outperforming the author of the dataset by 3.05%and the state-of-the-art technique by 1.45%.
基金This work was supported by the China Scholarship Council Scholarship,the National Key Research and Development Program of China(2017YFB0306400)the National Natural Science Foundation of China(62073069)the Deanship of Scientific Research(DSR)at King Abdulaziz University(RG-48-135-40).
文摘Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due time.It is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production systems.Two objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the problem.To obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy algorithm.The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers.Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.
基金supported by the National Natural Science Foundation of China(61773120)the National Natural Science Fund for Distinguished Young Scholars of China(61525304)+2 种基金the Foundation for the Author of National Excellent Doctoral Dissertation of China(2014-92)the Hunan Postgraduate Research Innovation Project(CX2018B022)the China Scholarship Council-Leiden University Scholarship。
文摘Inter-satellite link(ISL)scheduling is required by the BeiDou Navigation Satellite System(BDS)to guarantee the system ranging and communication performance.In the BDS,a great number of ISL scheduling instances must be addressed every day,which will certainly spend a lot of time via normal metaheuristics and hardly meet the quick-response requirements that often occur in real-world applications.To address the dual requirements of normal and quick-response ISL schedulings,a data-driven heuristic assisted memetic algorithm(DHMA)is proposed in this paper,which includes a high-performance memetic algorithm(MA)and a data-driven heuristic.In normal situations,the high-performance MA that hybridizes parallelism,competition,and evolution strategies is performed for high-quality ISL scheduling solutions over time.When in quick-response situations,the data-driven heuristic is performed to quickly schedule high-probability ISLs according to a prediction model,which is trained from the high-quality MA solutions.The main idea of the DHMA is to address normal and quick-response schedulings separately,while high-quality normal scheduling data are trained for quick-response use.In addition,this paper also presents an easy-to-understand ISL scheduling model and its NP-completeness.A seven-day experimental study with 10080 one-minute ISL scheduling instances shows the efficient performance of the DHMA in addressing the ISL scheduling in normal(in 84 hours)and quick-response(in 0.62 hour)situations,which can well meet the dual scheduling requirements in real-world BDS applications.
基金supporting by grant fund under the Strategic Scholarships for Frontier Research Network for the PhD Program Thai Doctoral degree
文摘A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.
基金Project(513300303)supported by the General Armament Department,China
文摘Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance of Gaussian process model.However,the common-used algorithm has the disadvantages of difficult determination of iteration steps,over-dependence of optimization effect on initial values,and easily falling into local optimum.To solve this problem,a method combining the Gaussian process with memetic algorithm was proposed.Based on this method,memetic algorithm was used to search the optimal hyper parameters of Gaussian process regression(GPR)model in the training process and form MA-GPR algorithms,and then the model was used to predict and test the results.When used in the marine long-range precision strike system(LPSS)battle effectiveness evaluation,the proposed MA-GPR model significantly improved the prediction accuracy,compared with the conjugate gradient method and the genetic algorithm optimization process.
基金supported by the National Natural Science Foundation of China(71171038)
文摘A memetic algorithm （MA） for a multi-mode resourceconstrained project scheduling problem （MRCPSP） is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The fitness function makes use of a mechanism called ＂strategic oscillation＂ to make the search process have a higher probability to visit solutions around a ＂feasible boundary＂. One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A detailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.
基金supported by the National Natural Science Foundation of China(No.62076225).
文摘This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time(EADHFSP-ST)that simultaneously optimizes the makespan and the energy consumption.We develop a mixed integer linear programming model to describe this problem and present a two-stage adaptive memetic algorithm(TAMA)with a surprisingly popular mechanism.First,a hybrid initialization strategy is designed based on the two optimization objectives to ensure the convergence and diversity of solutions.Second,multiple population co-evolutionary approaches are proposed for global search to escape from traditional cross-randomization and to balance exploration and exploitation.Third,considering that the memetic algorithm(MA)framework is less efficient due to the randomness in the selection of local search operators,TAMA is proposed to balance the local and global searches.The first stage accumulates more experience for updating the surprisingly popular algorithm(SPA)model to guide the second stage operator selection and ensures population convergence.The second stage gets rid of local optimization and designs an elite archive to ensure population diversity.Fourth,five problem-specific operators are designed,and non-critical path deceleration and right-shift strategies are designed for energy efficiency.Finally,to evaluate the performance of the proposed algorithm,multiple experiments are performed on a benchmark with 45 instances.The experimental results show that the proposed TAMA can solve the problem effectively.
基金This work was supported by the National Natural Science Foundation of China(No.52105244)Entrepreneurship and Innovation Support Plan of Chongqing for Returned Overseas Scholars(No.cx2023085).
文摘The heightened autonomy and robust adaptability inherent in a multi-robot system have proven pivotal in disaster search and rescue,agricultural irrigation,and environmental monitoring.This study addresses the coordination of multiple robots for the surveillance of various key target positions within an area.This involves the allocation of target positions among robots and the concurrent planning of routes for each robot.To tackle these challenges,we formulate a unified optimization model addressing both target allocation and route planning.Subsequently,we introduce an adaptive memetic algorithm featuring dual-level local search strategies.This algorithm operates independently among and within robots to effectively solve the optimization problem associated with surveillance.The proposed method’s efficacy is substantiated through comparative numerical experiments and simulated experiments involving diverse scales of robot teams and different target positions.
文摘Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network.This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms.The efficiency of the sensor node is energy bounded,acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors.Network management plays a significant role in wireless sensor networks,which was obsessed with the factors like the reliability of the network,resource management,energy-efficient routing,and scalability of services.The topology of the wireless sensor networks acts dri-ven factor for network efficiency which can be effectively maintained by perform-ing the clustering process effectively.More solutions and clustering algorithms have been offered by various researchers,but the concern of reduced efficiency in the routing process and network management still exists.This research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efficient clustering process in the wireless sensor networks.The memetic algorithm employs a local searching methodology to mitigate the premature convergence,while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain(i.e.,)best cluster head in the wireless sensor networks.The proposed hybrid algorithm is compared with the state of art clus-tering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time,energy consumption,throughput,etc.
基金the National Natural Science Foundation of China(No.62273193)the Talent Introducing Project of Hebei Agricultural University(Nos.KY201903 and YJ201953).
文摘This paper proposed a novel distributed memetic evolutionary model,where four modules distributed exploration,intensified exploitation,knowledge transfer,and evolutionary restart are coevolved to maximize their strengths and achieve superior global optimality.Distributed exploration evolves three independent populations by heterogenous operators.Intensified exploitation evolves an external elite archive in parallel with exploration to balance global and local searches.Knowledge transfer is based on a point-ring communication topology to share successful experiences among distinct search agents.Evolutionary restart adopts an adaptive perturbation strategy to control search diversity reasonably.Quantum computation is a newly emerging technique,which has powerful computing power and parallelized ability.Therefore,this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm,referred to as quantum-inspired distributed memetic algorithm(QDMA).In QDMA,individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum hyperspace.The QDMA integrates the superiorities of distributed,memetic,and quantum evolution.Computational experiments are carried out to evaluate the superior performance of QDMA.The results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum test.The superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model,but also to superior designs of each special component.
基金co-supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 61321002)the Projects of Major International (Regional) Joint Research Program NSFC (No. 61120106010)+1 种基金Beijing Education Committee Cooperation Building Foundation Project, the National Natural Science Foundation of China (No. 61304215)Beijing Outstanding Ph.D. Program Mentor (No. 20131000704)
文摘The problem of generating optimal paths for curvature-constrained unmanned aerial vehicles （UAVs） performing surveillance of multiple ground targets is addressed in this paper. UAVs are modeled as Dubins vehicles so that the constraints of UAVs＇ minimal turning radius can be taken into account. In view of the effective surveillance range of the sensors equipped on UAVs, the problem is formulated as a Dubins traveling salesman problem with neighborhood （DTSPN）. Considering its prohibitively high computational complexity, the Dubins paths in the sense of terminal heading relaxation are introduced to simplify the calculation of the Dubins distance, and a boundary-based encoding scheme is proposed to determine the visiting point of every target neighborhood. Then, an evolutionary algorithm is used to derive the optimal Dubins tour. To further enhance the quality of the solutions, a local search strategy based on approximate gradient is employed to improve the visiting points of target neighborhoods. Finally, by a minor modification to the individual encoding, the algorithm is easily extended to deal with other two more sophisticated DTSPN variants （multi-UAV scenario and multiple groups of targets scenario）. The performance of the algorithm is demonstrated through comparative experiments with other two state-of-the-art DTSPN algorithms identified in literature. Numerical simulations exhibit that the algorithm proposed in this paper can find high-quality solutions to the DTSPN with lower computational cost and produce significantly improved performance over the other algorithms.
基金supported by the National Natural Science Foundation of China (Nos. 91538204, 61425014, 61521091)National Key Research and Development Program of China (No. 2016YFB1200100)National Key Technology R&D Program of China (No. 2015BAG15B01)
文摘Due to rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we systematically explore the robustness of the Chinese air route network, and identify the vital edges which form the backbone of Chinese air transportation system.Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges, and hence the solution of this model is the set of vital edges. Counterintuitively,our results show that the most vital edges are not necessarily the edges of the highest topological importance, for which we provide an extensive explanation from the microscope view. Our findings also offer new insights to understanding and optimizing other real-world network systems.
基金co-supported by the National High-tech Research and Development Program of China (Grant No.2011AA110101)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 60921001)China Scholarship Council
文摘Conflict avoidance （CA） plays a crucial role in guaranteeing the airspace safety. The cur- rent approaches, mostly focusing on a short-term situation which eliminates conflicts via local adjust- ment, cannot provide a global solution. Recently, long-term conflict avoidance approaches, which are proposed to provide solutions via strategically planning traffic flow from a global view, have attracted more attentions. With consideration of the situation in China, there are thousands of flights per day and the air route network is large and complex, which makes the long-term problem to be a large-scale combinatorial optimization problem with complex constraints. To minimize the risk of premature convergence being faced by current approaches and obtain higher quality solutions, in this work, we present an effective strategic framework based on a memetic algorithm （MA）, which can markedly improve search capability via a combination of population-based global search and local improve- ments made by individuals. In addition, a specially designed local search operator and an adaptive local search frequency strategy are proposed to improve the solution quality. Furthermore, a fast genetic algorithm （GA） is presented as the global optimization method. Empirical studies using real traffic data of the Chinese air route network and daily flight plans show that our approach outper- formed the existing approaches including the GA .based approach and the cooperative coevolution based approach as well as some well-known memetic algorithm based approaches.
文摘The eigenface method that uses principal component analysis(PCA) has been the standard and popular method used in face recognition.This paper presents a PCA-memetic algorithm(PCA-MA) approach for feature selection.PCA has been extended by MAs where the former was used for feature extraction/dimensionality reduction and the latter exploited for feature selection.Simulations were performed over ORL and YaleB face databases using Euclidean norm as the classifier.It was found that as far as the recognition rate is concerned,PCA-MA completely outperforms the eigenface method.We compared the performance of PCA extended with genetic algorithm(PCA-GA) with our proposed PCA-MA method.The results also clearly established the supremacy of the PCA-MA method over the PCA-GA method.We further extended linear discriminant analysis(LDA) and kernel principal component analysis(KPCA) approaches with the MA and observed significant improvement in recognition rate with fewer features.This paper also compares the performance of PCA-MA,LDA-MA and KPCA-MA approaches.
基金Project supported by the National Natural Science Foundation of China (Nos. 61872123 and 61473263)the Zhejiang Provincial Natural Science Foundation,China (No. LR20F030002)。
文摘The use of unmanned aerial vehicles(UAVs) is becoming more commonplace in search-and-rescue tasks,but UAV search planning can be very complex due to limited response time, large search area, and multiple candidate search modes. In this paper, we present a UAV search planning problem where the search area is divided into a set of subareas and each subarea has a prior probability that the target is present in it. The problem aims to determine the search sequence of the subareas and the search mode for each subarea to maximize the probability of finding the target. We propose an adaptive memetic algorithm that combines a genetic algorithm with a set of local search procedures and dynamically determines which procedure to apply based on the past performance of the procedures measured in fitness improvement and diversity improvement during problem-solving. Computational experiments show that the proposed algorithm exhibits competitive performance compared to a set of state-of-the-art global search heuristics, non-adaptive memetic algorithms, and adaptive memetic algorithms on a wide set of problem instances.
文摘This work presents a memetic Shuffled Frog Leaping Algorithm(SFLA)based tuning approach of an Integral Sliding Mode Controller(ISMC)for a quadrotor type of Unmanned Aerial Vehicles(UAV).Based on the Newton–Euler formalism,a nonlinear dynamic model of the studied quadrotor is firstly established for control design purposes.Since the main parameters of the ISMC design are the gains of the sliding surfaces and signum functions of the switching control law,which are usually selected by repetitive and time-consuming trials-errors based procedures,a constrained optimization problem is formulated for the systematically tuning of these unknown variables.Under time-domain operating constraints,such an optimization-based tuning problem is effectively solved using the proposed SFLA metaheuristic with an empirical comparison to other evolutionary computation-and swarm intelligence-based algorithms such as the Crow Search Algorithm(CSA),Fractional Particle Swarm Optimization Memetic Algorithm(FPSOMA),Ant Bee Colony(ABC)and Harmony Search Algorithm(HSA).Numerical experiments are carried out for various sets of algorithms’parameters to achieve optimal gains of the sliding mode controllers for the altitude and attitude dynamics stabilization.Comparative studies revealed that the SFLA is a competitive and easily implemented algorithm with high performance in terms of robustness and non-premature convergence.Demonstrative results verified that the proposed metaheuristicsbased approach is a promising alternative for the systematic tuning of the effective design parameters in the integral sliding mode control framework.
基金Project (No. 60721062) supported by the National Creative Research Groups Science Foundation of China
文摘Based on the critical position of the endpoint quality prediction for basic oxygen furnaces （BOFs） in steelmaking, and the latest results in computational intelligence （C1）, this paper deals with the development of a novel memetic algorithm （MA） for neural network （NN） lcarnmg. Included in this is the integration of extremal optimization （EO） and Levenberg-Marquardt （LM） pradicnt search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.
文摘Purpose–The examination timetabling problem is an NP-hard problem.A large number of approaches for this problem are developed to find more appropriate search strategies.Hyper-heuristic is a kind of representative methods.In hyper-heuristic,the high-level search is executed to construct heuristic lists by traditional methods(such as Tabu search,variable neighborhoods and so on).The purpose of this paper is to apply the evolutionary strategy instead of traditional methods for high-level search to improve the capability of global search.Design/methodology/approach–This paper combines hyper-heuristic with evolutionary strategy to solve examination timetabling problems.First,four graph coloring heuristics are employed to construct heuristic lists.Within the evolutionary algorithm framework,the iterative initialization is utilized to improve the number of feasible solutions in the population;meanwhile,the crossover and mutation operators are applied to find potential heuristic lists in the heuristic space(high-level search).At last,two local search methods are combined to optimize the feasible solutions in the solution space(low-level search).Findings–Experimental results demonstrate that the proposed approach obtains competitive results and outperforms the compared approaches on some benchmark instances.Originality/value–The contribution of this paper is the development of a framework which combines evolutionary algorithm and hyper-heuristic for examination timetabling problems.
文摘In the healthcare system,the Internet of Things(IoT)based distributed systems play a vital role in transferring the medical-related documents and information among the organizations to reduce the replication in medical tests.This datum is sensitive,and hence security is a must in transforming the sensational contents.In this paper,an Evolutionary Algorithm,namely the Memetic Algorithm is used for encrypting the text messages.The encrypted information is then inserted into the medical images using Discrete Wavelet Transform 1 level and 2 levels.The reverse method of the Memetic Algorithm is implemented when extracting a hidden message from the encoded letter.To show its precision,equivalent to five RGB images and five Grayscale images are used to test the proposed algorithm.The results of the proposed algorithm were analyzed using statistical methods,and the proposed algorithm showed the importance of data transfer in healthcare systems in a stable environment.In the future,to embed the privacy-preserving of medical data,it can be extended with blockchain technology.
