Response surface methodology was used to optimize the medium for antifungal active substance production from Streptomyces lydicus E12 in flask cultivation.Initially,the component factors,which influence antifungal sub...Response surface methodology was used to optimize the medium for antifungal active substance production from Streptomyces lydicus E12 in flask cultivation.Initially,the component factors,which influence antifungal substance production,were studied by varying one factor at a time.Starch,soybean cake powder,K_2HPO_4·3H_2O and MgSO_4·7H_2O were found to have a significant effect on the production of antifungal substances by the traditional design.Then,a Box-Behnken design was applied for further optimization.A quadratic model was found to fit antifungal active substance production.The analysis revealed that the optimum values of the tested variable were starch 84.96 g/L,soybean cake powder 4.13 g/L,glucose 5 g/L,MgSO_4·7H_2O 1.23 g/L,K_2HPO_4·3H_2O2.14 g/L and NaCl 0.5 g/L.The test result of 67.44%antifungal inhibition agreed with the prediction and increased by 14.28%in comparison with the basal medium.展开更多
Yeast strain Y68 producing high level of pullulan was isolated from the phyton collected in Toulouse, France. This strain was identified to be Rhodotorula bacarum by BIOLOG analysis. This is the first report that pull...Yeast strain Y68 producing high level of pullulan was isolated from the phyton collected in Toulouse, France. This strain was identified to be Rhodotorula bacarum by BIOLOG analysis. This is the first report that pullulan was produced by Rhodotorula bacarum. The optimal medium (g L -1) for pullulan production by this strain was 80 glucose, 20 soybean cake hydrolysate, 5 K 2HPO 4, 1 NaCl, 0.2 MgSO 4·7H 2O, 0.6 (NH 4) 2SO 4, pH 7.0. Under this condition, 54 g L -1 pullulan was produced within 60 h at 30 ℃. Pullulan is a better starting material for producing marine prodrugs.展开更多
[Objectives] This study was conducted to further improve the application of Oenococcus oeni in the wine brewing industry. [Methods] O. oeni was isolated, screened, and identified, and medium optimization was performed...[Objectives] This study was conducted to further improve the application of Oenococcus oeni in the wine brewing industry. [Methods] O. oeni was isolated, screened, and identified, and medium optimization was performed to determine the best medium, temperature and oxygen conditions. [Results] O. oeni grew better in mFT80 medium, and the culture conditions were 25 ℃ and an anaerobic condition. [Conclusions] This study provides a reference for the development of China’s wine industry.展开更多
Response surface methodology (RSM) was used to optimize the fermentation medium for enhancing pyruvic acid production by Torulopsis glabrata TP19. In the first step of optimization, with Plackett-Burman design, ammoni...Response surface methodology (RSM) was used to optimize the fermentation medium for enhancing pyruvic acid production by Torulopsis glabrata TP19. In the first step of optimization, with Plackett-Burman design, ammonium sulfate, glucose and nicotinic acid were found to be the important factors affecting pyruvic acid production significantly. In the second step, a 23 full factorial central composite design and RSM were applied to determine the optimal concentration of each significant variable. A second-order polynomial was determined by the multiple regression analysis of the experimental data. The optimum values for the critical components were obtained as follows: ammonium sulfate 0.7498 (10.75 g/L), glucose 0.9383 (109.38 g/L) and nicotinic acid 0.3633 (7.86 mg/L) with a predicted value of maximum pyruvic acid production of 42.2 g/L. Under the optimal conditions, the practical pyruvic acid production was 42.4 g/L. The determination coefficient (R2) was 0.9483, which ensures adequate credibility of the model. By scaling up fermentation from flask to jar fermentor, we obtained promising results.展开更多
In order to screen a high-yield spergualin-production strain and an optimal fermentation medium,large numbers of isolates were selected after ultraviolet(UV)mutation and self-tolerant mutation of Bacillus laterosporus...In order to screen a high-yield spergualin-production strain and an optimal fermentation medium,large numbers of isolates were selected after ultraviolet(UV)mutation and self-tolerant mutation of Bacillus laterosporus A7.A high-yield strain A-94-7 was obtained and the spergualin productivity was 4.1-fold of the parent strain.The genetic stability of the high-yield strain Bacillus laterosporus A-94-7 was very good.After fermentation medium optimization,Bacillus laterosporus A-94-7 was able to produce 380 mg?L-1.The spergualin productivity in Bacillus laterosporus A7 was increased 4.75-fold by mutation and medium optimization.展开更多
Response surface methodology (RSM) is used to optimize the medium of Tetraselmis sp.-1 which is cell fused microalgae capable of growing under mixotrophic condition. Empirical models are developed to describe the rela...Response surface methodology (RSM) is used to optimize the medium of Tetraselmis sp.-1 which is cell fused microalgae capable of growing under mixotrophic condition. Empirical models are developed to describe the relationships between the operating variables (glucose, urea, sodium dehydrogenate phosphate, sodium chloride) and responses (cell density). Statistical analysis indicates that glucose and urea have significant effects on the microalgae cell density, but other two factors (sodium dehydrogenate phosphate, sodium chloride) have no obvious effect. The path of steepest ascent is used to approach the optimal region of medium composition. Optimal cell density (2.638 g dry weight/L) was reached when the operating conditions were glucose concentration (30.75 %), urea concentration (0.440 g/L), sodium dehydrogenate phosphate (15 mg/L) and sodium chloride (28 g/L).展开更多
Response surface methodology was used to optimize a medium for a red-pigmented marine bacterium S-9801 strain (Flavobacterium sp.). In the first optimization step the influence of yeast extract, peptone, glucose and s...Response surface methodology was used to optimize a medium for a red-pigmented marine bacterium S-9801 strain (Flavobacterium sp.). In the first optimization step the influence of yeast extract, peptone, glucose and sodium chloride on pigment production was evaluated using a fractional factorial design. Pigment production was positively influenced by glucose and sodium chloride while other components had no significant effect. In the second step the path of steepest ascent was used to approach the optimal region of the medium composition. In the third step the optimal concentration of glucose and sodium chloride was determined by a central composite design and response analysis. The optimized medium allowed pigment production (A 535～650) to be increased from 0.137 to0.559, being 320% higher than the original medium.展开更多
A mutant (GQQ-M6) of a Sponge-Derived streptomyces sp. GQQ-10 obtained by UV-induced mutation was used for producing prodiginines (PGs). Single factor experiments and orthogonal array design (OAD) methods were employe...A mutant (GQQ-M6) of a Sponge-Derived streptomyces sp. GQQ-10 obtained by UV-induced mutation was used for producing prodiginines (PGs). Single factor experiments and orthogonal array design (OAD) methods were employed for medium optimization. In the single factor method, the effects of soluble starch, glucose, soybean flour, yeast extract and sodium acetate on PGs production were investigated individually. In the subsequent OAD experiments, the concentrations of these 5 key nutritional components combined with salinity were further adjusted. The mutant strain GQQ-M6 gave a 2.2-fold higher PGs production than that of the parent strain; OAD experiments offered a PGs yield of 61mg L-1, which was 10 times higher than that of the initial GQQ-10 strain under the original cultivation mode.展开更多
In this paper,given the shortcomings of jellyfish search algorithmwith low search ability in the early stage and easy to fall into local optimal solution,this paper introduces adaptive weight function and elite strate...In this paper,given the shortcomings of jellyfish search algorithmwith low search ability in the early stage and easy to fall into local optimal solution,this paper introduces adaptive weight function and elite strategy,improving the global search scope in the early stage and the ability to refine the local development in the later stage.In the numerical study,the benchmark problem of dimensional optimization with a 10-bar truss structure and simultaneous dimensional shape optimization with a 15-bar truss structure is adopted,and the corresponding penalty method is used for constraint treatment.The test results show that the improved jellyfish search algorithm can provide better truss sections as well as weights.Because when the steel main truss of the large-span covered bridge is lifted,the site is limited and the large lifting equipment cannot enter the site,and the original structure does not meet the problem of stress concentration and large deformation of the bolt group,so the spreader is used to lift,and the improved jellyfish search algorithm is introduced into the design optimization of the spreader.The results show that the improved jellyfish algorithm can efficiently and accurately find out the optimal shape and weight of the spreader,and throughMidas Civil simulation,the spreader used canmeet the requirements of weight and safety.展开更多
We propose a combined shape and topology optimization approach in this research for 3D acoustics by using the isogeometric boundary element method with subdivision surfaces.The existing structural optimization methods...We propose a combined shape and topology optimization approach in this research for 3D acoustics by using the isogeometric boundary element method with subdivision surfaces.The existing structural optimization methods mainly contain shape and topology schemes,with the former changing the surface geometric profile of the structure and the latter changing thematerial distribution topology or hole topology of the structure.In the present acoustic performance optimization,the coordinates of the control points in the subdivision surfaces fine mesh are selected as the shape design parameters of the structure,the artificial density of the sound absorbing material covered on the structure surface is set as the topology design parameter,and the combined topology and shape optimization approach is established through the sound field analysis of the subdivision surfaces boundary element method as a bridge.The topology and shape sensitivities of the approach are calculated using the adjoint variable method,which ensures the efficiency of the optimization.The geometric jaggedness and material distribution discontinuities that appear in the optimization process are overcome to a certain degree by the multiresolution method and solid isotropic material with penalization.Numerical examples are given to validate the effectiveness of the presented optimization approach.展开更多
To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the ...To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the comprehensive artillery internal ballistic dynamics(AIBD)model,based on propellant combustion,rotation band engraving,projectile axial motion,and rifling wear models,was established and validated.This model was systematically decomposed into subsystems from a system engineering perspective.The study then detailed the MS-MDO methodology,which included Stage I(MDO stage)employing an improved collaborative optimization method for consistent design variables,and Stage II(Performance Optimization)focusing on the independent optimization of local design variables and performance metrics.The methodology was applied to the AIBD problem.Results demonstrated that the MS-MDO method in Stage I effectively reduced iteration and evaluation counts,thereby accelerating system-level convergence.Meanwhile,Stage II optimization markedly enhanced overall performance.These comprehensive evaluation results affirmed the effectiveness of the MS-MDO method.展开更多
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
This research study aims to enhance the optimization performance of a newly emerged Aquila Optimization algorithm by incorporating chaotic sequences rather than using uniformly generated Gaussian random numbers.This w...This research study aims to enhance the optimization performance of a newly emerged Aquila Optimization algorithm by incorporating chaotic sequences rather than using uniformly generated Gaussian random numbers.This work employs 25 different chaotic maps under the framework of Aquila Optimizer.It considers the ten best chaotic variants for performance evaluation on multidimensional test functions composed of unimodal and multimodal problems,which have yet to be studied in past literature works.It was found that Ikeda chaotic map enhanced Aquila Optimization algorithm yields the best predictions and becomes the leading method in most of the cases.To test the effectivity of this chaotic variant on real-world optimization problems,it is employed on two constrained engineering design problems,and its effectiveness has been verified.Finally,phase equilibrium and semi-empirical parameter estimation problems have been solved by the proposed method,and respective solutions have been compared with those obtained from state-of-art optimizers.It is observed that CH01 can successfully cope with the restrictive nonlinearities and nonconvexities of parameter estimation and phase equilibrium problems,showing the capabilities of yielding minimum prediction error values of no more than 0.05 compared to the remaining algorithms utilized in the performance benchmarking process.展开更多
This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optima...This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.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
This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures.These structures,commonly encounter...This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures.These structures,commonly encountered in engineering applications,often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design variables.As a result,sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization challenges.Over the past several decades,topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical backgrounds.In comparison to the large-scale equation solution,sensitivity analysis,graphics post-processing,etc.,the progress of the sequential approximation functions and their corresponding optimizersmake sluggish progress.Researchers,particularly novices,pay special attention to their difficulties with a particular problem.Thus,this paper provides an overview of sequential approximation functions,related literature on topology optimization methods,and their applications.Starting from optimality criteria and sequential linear programming,the other sequential approximate optimizations are introduced by employing Taylor expansion and intervening variables.In addition,recent advancements have led to the emergence of approaches such as Augmented Lagrange,sequential approximate integer,and non-gradient approximation are also introduced.By highlighting real-world applications and case studies,the paper not only demonstrates the practical relevance of these methods but also underscores the need for continued exploration in this area.Furthermore,to provide a comprehensive overview,this paper offers several novel developments that aim to illuminate potential directions for future research.展开更多
Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as ...Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.展开更多
Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety.This paper proposes a modified back analysis method with multi-objective ...Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety.This paper proposes a modified back analysis method with multi-objective optimization procedure,which enables a real-time prediction of horizontal displacement of retaining pile during construction.As opposed to the traditional stage-by-stage back analysis,time series monitoring data till the current excavation stage are utilized to form a multi-objective function.Then,the multi-objective particle swarm optimization (MOPSO) algorithm is applied for parameter identification.The optimized model parameters are immediately adopted to predict the excavation-induced pile deformation in the continuous construction stages.To achieve efficient parameter optimization and real-time prediction of system behavior,the back propagation neural network (BPNN) is established to substitute the finite element model,which is further implemented together with MOPSO for automatic operation.The proposed approach is applied in the Taihu tunnel excavation project,where the effectiveness of the method is demonstrated via the comparisons with the site monitoring data.The method is reliable with a prediction accuracy of more than 90%.Moreover,different optimization algorithms,including non-dominated sorting genetic algorithm (NSGA-II),Pareto Envelope-based Selection Algorithm II (PESA-II) and MOPSO,are compared,and their influences on the prediction accuracy at different excavation stages are studied.The results show that MOPSO has the best performance for high dimensional optimization task.展开更多
The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy ...The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.展开更多
基金Supported by the National Basic Research Program of China (‘‘973’’ Program:2014CB745100)the National Natural Science Foundation of China (No.21576201 and No.21176183)
文摘Response surface methodology was used to optimize the medium for antifungal active substance production from Streptomyces lydicus E12 in flask cultivation.Initially,the component factors,which influence antifungal substance production,were studied by varying one factor at a time.Starch,soybean cake powder,K_2HPO_4·3H_2O and MgSO_4·7H_2O were found to have a significant effect on the production of antifungal substances by the traditional design.Then,a Box-Behnken design was applied for further optimization.A quadratic model was found to fit antifungal active substance production.The analysis revealed that the optimum values of the tested variable were starch 84.96 g/L,soybean cake powder 4.13 g/L,glucose 5 g/L,MgSO_4·7H_2O 1.23 g/L,K_2HPO_4·3H_2O2.14 g/L and NaCl 0.5 g/L.The test result of 67.44%antifungal inhibition agreed with the prediction and increased by 14.28%in comparison with the basal medium.
基金National Natural Science Foundation of China(Grant No.39970005)for its financial support
文摘Yeast strain Y68 producing high level of pullulan was isolated from the phyton collected in Toulouse, France. This strain was identified to be Rhodotorula bacarum by BIOLOG analysis. This is the first report that pullulan was produced by Rhodotorula bacarum. The optimal medium (g L -1) for pullulan production by this strain was 80 glucose, 20 soybean cake hydrolysate, 5 K 2HPO 4, 1 NaCl, 0.2 MgSO 4·7H 2O, 0.6 (NH 4) 2SO 4, pH 7.0. Under this condition, 54 g L -1 pullulan was produced within 60 h at 30 ℃. Pullulan is a better starting material for producing marine prodrugs.
文摘[Objectives] This study was conducted to further improve the application of Oenococcus oeni in the wine brewing industry. [Methods] O. oeni was isolated, screened, and identified, and medium optimization was performed to determine the best medium, temperature and oxygen conditions. [Results] O. oeni grew better in mFT80 medium, and the culture conditions were 25 ℃ and an anaerobic condition. [Conclusions] This study provides a reference for the development of China’s wine industry.
文摘Response surface methodology (RSM) was used to optimize the fermentation medium for enhancing pyruvic acid production by Torulopsis glabrata TP19. In the first step of optimization, with Plackett-Burman design, ammonium sulfate, glucose and nicotinic acid were found to be the important factors affecting pyruvic acid production significantly. In the second step, a 23 full factorial central composite design and RSM were applied to determine the optimal concentration of each significant variable. A second-order polynomial was determined by the multiple regression analysis of the experimental data. The optimum values for the critical components were obtained as follows: ammonium sulfate 0.7498 (10.75 g/L), glucose 0.9383 (109.38 g/L) and nicotinic acid 0.3633 (7.86 mg/L) with a predicted value of maximum pyruvic acid production of 42.2 g/L. Under the optimal conditions, the practical pyruvic acid production was 42.4 g/L. The determination coefficient (R2) was 0.9483, which ensures adequate credibility of the model. By scaling up fermentation from flask to jar fermentor, we obtained promising results.
基金Supported by Program for Innovative Research Team of Northeast Agricultural University
文摘In order to screen a high-yield spergualin-production strain and an optimal fermentation medium,large numbers of isolates were selected after ultraviolet(UV)mutation and self-tolerant mutation of Bacillus laterosporus A7.A high-yield strain A-94-7 was obtained and the spergualin productivity was 4.1-fold of the parent strain.The genetic stability of the high-yield strain Bacillus laterosporus A-94-7 was very good.After fermentation medium optimization,Bacillus laterosporus A-94-7 was able to produce 380 mg?L-1.The spergualin productivity in Bacillus laterosporus A7 was increased 4.75-fold by mutation and medium optimization.
文摘Response surface methodology (RSM) is used to optimize the medium of Tetraselmis sp.-1 which is cell fused microalgae capable of growing under mixotrophic condition. Empirical models are developed to describe the relationships between the operating variables (glucose, urea, sodium dehydrogenate phosphate, sodium chloride) and responses (cell density). Statistical analysis indicates that glucose and urea have significant effects on the microalgae cell density, but other two factors (sodium dehydrogenate phosphate, sodium chloride) have no obvious effect. The path of steepest ascent is used to approach the optimal region of medium composition. Optimal cell density (2.638 g dry weight/L) was reached when the operating conditions were glucose concentration (30.75 %), urea concentration (0.440 g/L), sodium dehydrogenate phosphate (15 mg/L) and sodium chloride (28 g/L).
文摘Response surface methodology was used to optimize a medium for a red-pigmented marine bacterium S-9801 strain (Flavobacterium sp.). In the first optimization step the influence of yeast extract, peptone, glucose and sodium chloride on pigment production was evaluated using a fractional factorial design. Pigment production was positively influenced by glucose and sodium chloride while other components had no significant effect. In the second step the path of steepest ascent was used to approach the optimal region of the medium composition. In the third step the optimal concentration of glucose and sodium chloride was determined by a central composite design and response analysis. The optimized medium allowed pigment production (A 535～650) to be increased from 0.137 to0.559, being 320% higher than the original medium.
基金supported by the National Natural Science Foundation of China (Nos.30973627 and 30772640)the public projects of the State Oceanic Administration (No.2010418022-3)+1 种基金the Program for Changjiang Scholars and Innovative Research Team in University (No.IRT0944)the Natural Science Fund of Shandong Province,P.R.China (No.ZR2009CZ016)
文摘A mutant (GQQ-M6) of a Sponge-Derived streptomyces sp. GQQ-10 obtained by UV-induced mutation was used for producing prodiginines (PGs). Single factor experiments and orthogonal array design (OAD) methods were employed for medium optimization. In the single factor method, the effects of soluble starch, glucose, soybean flour, yeast extract and sodium acetate on PGs production were investigated individually. In the subsequent OAD experiments, the concentrations of these 5 key nutritional components combined with salinity were further adjusted. The mutant strain GQQ-M6 gave a 2.2-fold higher PGs production than that of the parent strain; OAD experiments offered a PGs yield of 61mg L-1, which was 10 times higher than that of the initial GQQ-10 strain under the original cultivation mode.
基金the National Natural Science Foundation of China(Grant No.51305372)the Open Fund Project of the Transportation Infrastructure Intelligent Management and Maintenance Engineering Technology Center of Xiamen City(Grant No.TCIMI201803)the Project of the 2011 Collaborative Innovation Center of Fujian Province(Grant No.2016BJC019).
文摘In this paper,given the shortcomings of jellyfish search algorithmwith low search ability in the early stage and easy to fall into local optimal solution,this paper introduces adaptive weight function and elite strategy,improving the global search scope in the early stage and the ability to refine the local development in the later stage.In the numerical study,the benchmark problem of dimensional optimization with a 10-bar truss structure and simultaneous dimensional shape optimization with a 15-bar truss structure is adopted,and the corresponding penalty method is used for constraint treatment.The test results show that the improved jellyfish search algorithm can provide better truss sections as well as weights.Because when the steel main truss of the large-span covered bridge is lifted,the site is limited and the large lifting equipment cannot enter the site,and the original structure does not meet the problem of stress concentration and large deformation of the bolt group,so the spreader is used to lift,and the improved jellyfish search algorithm is introduced into the design optimization of the spreader.The results show that the improved jellyfish algorithm can efficiently and accurately find out the optimal shape and weight of the spreader,and throughMidas Civil simulation,the spreader used canmeet the requirements of weight and safety.
基金supported by the National Natural Science Foundation of China (NSFC)under Grant Nos.12172350,11772322 and 11702238。
文摘We propose a combined shape and topology optimization approach in this research for 3D acoustics by using the isogeometric boundary element method with subdivision surfaces.The existing structural optimization methods mainly contain shape and topology schemes,with the former changing the surface geometric profile of the structure and the latter changing thematerial distribution topology or hole topology of the structure.In the present acoustic performance optimization,the coordinates of the control points in the subdivision surfaces fine mesh are selected as the shape design parameters of the structure,the artificial density of the sound absorbing material covered on the structure surface is set as the topology design parameter,and the combined topology and shape optimization approach is established through the sound field analysis of the subdivision surfaces boundary element method as a bridge.The topology and shape sensitivities of the approach are calculated using the adjoint variable method,which ensures the efficiency of the optimization.The geometric jaggedness and material distribution discontinuities that appear in the optimization process are overcome to a certain degree by the multiresolution method and solid isotropic material with penalization.Numerical examples are given to validate the effectiveness of the presented optimization approach.
基金supported by the“National Natural Science Foundation of China”(Grant Nos.52105106,52305155)the“Jiangsu Province Natural Science Foundation”(Grant Nos.BK20210342,BK20230904)the“Young Elite Scientists Sponsorship Programby CAST”(Grant No.2023JCJQQT061).
文摘To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the comprehensive artillery internal ballistic dynamics(AIBD)model,based on propellant combustion,rotation band engraving,projectile axial motion,and rifling wear models,was established and validated.This model was systematically decomposed into subsystems from a system engineering perspective.The study then detailed the MS-MDO methodology,which included Stage I(MDO stage)employing an improved collaborative optimization method for consistent design variables,and Stage II(Performance Optimization)focusing on the independent optimization of local design variables and performance metrics.The methodology was applied to the AIBD problem.Results demonstrated that the MS-MDO method in Stage I effectively reduced iteration and evaluation counts,thereby accelerating system-level convergence.Meanwhile,Stage II optimization markedly enhanced overall performance.These comprehensive evaluation results affirmed the effectiveness of the MS-MDO method.
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This research study aims to enhance the optimization performance of a newly emerged Aquila Optimization algorithm by incorporating chaotic sequences rather than using uniformly generated Gaussian random numbers.This work employs 25 different chaotic maps under the framework of Aquila Optimizer.It considers the ten best chaotic variants for performance evaluation on multidimensional test functions composed of unimodal and multimodal problems,which have yet to be studied in past literature works.It was found that Ikeda chaotic map enhanced Aquila Optimization algorithm yields the best predictions and becomes the leading method in most of the cases.To test the effectivity of this chaotic variant on real-world optimization problems,it is employed on two constrained engineering design problems,and its effectiveness has been verified.Finally,phase equilibrium and semi-empirical parameter estimation problems have been solved by the proposed method,and respective solutions have been compared with those obtained from state-of-art optimizers.It is observed that CH01 can successfully cope with the restrictive nonlinearities and nonconvexities of parameter estimation and phase equilibrium problems,showing the capabilities of yielding minimum prediction error values of no more than 0.05 compared to the remaining algorithms utilized in the performance benchmarking process.
基金King Saud University for funding this research through Researchers Supporting Program Number(RSPD2023R704),King Saud University,Riyadh,Saudi Arabia.
文摘This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.
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financially supported by the National Key R&D Program (2022YFB4201302)Guang Dong Basic and Applied Basic Research Foundation (2022A1515240057)the Huaneng Technology Funds (HNKJ20-H88).
文摘This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures.These structures,commonly encountered in engineering applications,often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design variables.As a result,sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization challenges.Over the past several decades,topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical backgrounds.In comparison to the large-scale equation solution,sensitivity analysis,graphics post-processing,etc.,the progress of the sequential approximation functions and their corresponding optimizersmake sluggish progress.Researchers,particularly novices,pay special attention to their difficulties with a particular problem.Thus,this paper provides an overview of sequential approximation functions,related literature on topology optimization methods,and their applications.Starting from optimality criteria and sequential linear programming,the other sequential approximate optimizations are introduced by employing Taylor expansion and intervening variables.In addition,recent advancements have led to the emergence of approaches such as Augmented Lagrange,sequential approximate integer,and non-gradient approximation are also introduced.By highlighting real-world applications and case studies,the paper not only demonstrates the practical relevance of these methods but also underscores the need for continued exploration in this area.Furthermore,to provide a comprehensive overview,this paper offers several novel developments that aim to illuminate potential directions for future research.
文摘Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.
基金supported by the National Natural Science Foundation of China(Grant Nos.52208380 and 51979270)the Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences(Grant No.SKLGME021022).
文摘Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety.This paper proposes a modified back analysis method with multi-objective optimization procedure,which enables a real-time prediction of horizontal displacement of retaining pile during construction.As opposed to the traditional stage-by-stage back analysis,time series monitoring data till the current excavation stage are utilized to form a multi-objective function.Then,the multi-objective particle swarm optimization (MOPSO) algorithm is applied for parameter identification.The optimized model parameters are immediately adopted to predict the excavation-induced pile deformation in the continuous construction stages.To achieve efficient parameter optimization and real-time prediction of system behavior,the back propagation neural network (BPNN) is established to substitute the finite element model,which is further implemented together with MOPSO for automatic operation.The proposed approach is applied in the Taihu tunnel excavation project,where the effectiveness of the method is demonstrated via the comparisons with the site monitoring data.The method is reliable with a prediction accuracy of more than 90%.Moreover,different optimization algorithms,including non-dominated sorting genetic algorithm (NSGA-II),Pareto Envelope-based Selection Algorithm II (PESA-II) and MOPSO,are compared,and their influences on the prediction accuracy at different excavation stages are studied.The results show that MOPSO has the best performance for high dimensional optimization task.
基金Project supported by the Zhejiang Provincial Natural Science Foundation (Grant No.LQ20F020011)the Gansu Provincial Foundation for Distinguished Young Scholars (Grant No.23JRRA766)+1 种基金the National Natural Science Foundation of China (Grant No.62162040)the National Key Research and Development Program of China (Grant No.2020YFB1713600)。
文摘The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.
