The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powe...The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.展开更多
The artificial bee colony （ABC） algorithm is a sim- ple and effective global optimization algorithm which has been successfully applied in practical optimization problems of various fields. However, the algorithm is...The artificial bee colony （ABC） algorithm is a sim- ple and effective global optimization algorithm which has been successfully applied in practical optimization problems of various fields. However, the algorithm is still insufficient in balancing ex- ploration and exploitation. To solve this problem, we put forward an improved algorithm with a comprehensive search mechanism. The search mechanism contains three main strategies. Firstly, the heuristic Gaussian search strategy composed of three different search equations is proposed for the employed bees, which fully utilizes and balances the exploration and exploitation of the three different search equations by introducing the selectivity probability P,. Secondly, in order to improve the search accuracy, we propose the Gbest-guided neighborhood search strategy for onlooker bees to improve the exploitation performance of ABC. Thirdly, the self- adaptive population perturbation strategy for the current colony is used by random perturbation or Gaussian perturbation to en- hance the diversity of the population. In addition, to improve the quality of the initial population, we introduce the chaotic opposition- based learning method for initialization. The experimental results and Wilcoxon signed ranks test based on 27 benchmark func- tions show that the proposed algorithm, especially for solving high dimensional and complex function optimization problems, has a higher convergence speed and search precision than ABC and three other current ABC-based algorithms.展开更多
It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optima...It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optimal combination under various constraints not only involves numerical calculations but also is an NP-hard combinatorial problem.To solve the problem,an adaptive genetic algorithm based on cluster search,which is divided into two phases,is put forward.In the first phase,according to the density,all individuals can be homogeneously scattered over the whole solution space through crossover and mutation and better individuals are collected as candidate cluster centres.In the second phase,the search is confined to the neighbourhood of some selected possible solutions to accurately solve with cluster radius decreasing slowly,meanwhile all clusters continuously move to better regions until all the peaks in the question space is searched.This algorithm can efficiently solve the combination problem.Taking the optimization on decision-making of aircraft maintenance by the algorithm for an example,maintenance which combines multiple parts or tasks can significantly enhance economic benefit when the halt cost is rather high.展开更多
Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical ...Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.展开更多
Reinforcement inside the concrete is protected from corrosion and its damages until several years after the construction. After corrosion initiation, the cross section of reinforcement begins to reduce and often load ...Reinforcement inside the concrete is protected from corrosion and its damages until several years after the construction. After corrosion initiation, the cross section of reinforcement begins to reduce and often load bearing of the reinforced concrete structure will be reduced significantly. Corrosion of reinforcements in concrete in polluted and contaminated areas can occur in two ways: chloride and carbonation. In this work, meta-heuristic approach of charged system search(CSS) is used to calculate corrosion occurrence probability due to chloride ions penetration. The model efficiency is verified by comparing the available examples in technical literature and results of Monte Carlo analysis. According to the analyses performed, using different probabilistic distributions regardless of probabilistic moments based on real distribution leads to diverse results. In addition, influence of each effective parameter in corrosion occurrence varies by changing other parameters.展开更多
To overcome the limitations of traditional force aggregation methods,this paper proposes a novel clustering model integrating the self-adaptive tent chaos search ant lion optimizer(SATC-ALO)and the self-organizing map...To overcome the limitations of traditional force aggregation methods,this paper proposes a novel clustering model integrating the self-adaptive tent chaos search ant lion optimizer(SATC-ALO)and the self-organizing map(SOM)network.The model introduces a hybrid distance calculation method to measure inter-target distances and enhances the ant lion optimization algorithm through tent chaos sequences,adaptive tent chaos search,tournament selection,and logistic chaos sequences.Aggregation accuracy is evaluated using minimum quantization error and confidence value for the SOM neural network.The model is resolved using SATC-ALO and SOM independently,with experiments demonstrating that SOM achieves fast and accurate grouping,while SATC-ALO offers higher precision but requires longer computational runtime,making it more suitable for hybrid approaches.Both methods are validated as practical solutions for force aggregation tasks.展开更多
基金supported by the National Natural Science Foundation of China(61271250)
文摘The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.
基金supported by the Aviation Science Foundation of China(20105196016)the Postdoctoral Science Foundation of China(2012M521807)
文摘The artificial bee colony （ABC） algorithm is a sim- ple and effective global optimization algorithm which has been successfully applied in practical optimization problems of various fields. However, the algorithm is still insufficient in balancing ex- ploration and exploitation. To solve this problem, we put forward an improved algorithm with a comprehensive search mechanism. The search mechanism contains three main strategies. Firstly, the heuristic Gaussian search strategy composed of three different search equations is proposed for the employed bees, which fully utilizes and balances the exploration and exploitation of the three different search equations by introducing the selectivity probability P,. Secondly, in order to improve the search accuracy, we propose the Gbest-guided neighborhood search strategy for onlooker bees to improve the exploitation performance of ABC. Thirdly, the self- adaptive population perturbation strategy for the current colony is used by random perturbation or Gaussian perturbation to en- hance the diversity of the population. In addition, to improve the quality of the initial population, we introduce the chaotic opposition- based learning method for initialization. The experimental results and Wilcoxon signed ranks test based on 27 benchmark func- tions show that the proposed algorithm, especially for solving high dimensional and complex function optimization problems, has a higher convergence speed and search precision than ABC and three other current ABC-based algorithms.
基金supported by the National Natural Science Foundation of China(6107901361079014+4 种基金61403198)the National Natural Science Funds and Civil Aviaiton Mutual Funds(U1533128U1233114)the Programs of Natural Science Foundation of China and China Civil Aviation Joint Fund(60939003)the Natural Science Foundation of Jiangsu Province in China(BK2011737)
文摘It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optimal combination under various constraints not only involves numerical calculations but also is an NP-hard combinatorial problem.To solve the problem,an adaptive genetic algorithm based on cluster search,which is divided into two phases,is put forward.In the first phase,according to the density,all individuals can be homogeneously scattered over the whole solution space through crossover and mutation and better individuals are collected as candidate cluster centres.In the second phase,the search is confined to the neighbourhood of some selected possible solutions to accurately solve with cluster radius decreasing slowly,meanwhile all clusters continuously move to better regions until all the peaks in the question space is searched.This algorithm can efficiently solve the combination problem.Taking the optimization on decision-making of aircraft maintenance by the algorithm for an example,maintenance which combines multiple parts or tasks can significantly enhance economic benefit when the halt cost is rather high.
文摘Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.
文摘Reinforcement inside the concrete is protected from corrosion and its damages until several years after the construction. After corrosion initiation, the cross section of reinforcement begins to reduce and often load bearing of the reinforced concrete structure will be reduced significantly. Corrosion of reinforcements in concrete in polluted and contaminated areas can occur in two ways: chloride and carbonation. In this work, meta-heuristic approach of charged system search(CSS) is used to calculate corrosion occurrence probability due to chloride ions penetration. The model efficiency is verified by comparing the available examples in technical literature and results of Monte Carlo analysis. According to the analyses performed, using different probabilistic distributions regardless of probabilistic moments based on real distribution leads to diverse results. In addition, influence of each effective parameter in corrosion occurrence varies by changing other parameters.
基金supported by the Youth Talent Support Program of Xi’an Association for Science and Technology(0959202513098)the National Natural Science Foundation of China(62106284)the Natural Science Foundation of Shanxi Province(2021JQ370).
文摘To overcome the limitations of traditional force aggregation methods,this paper proposes a novel clustering model integrating the self-adaptive tent chaos search ant lion optimizer(SATC-ALO)and the self-organizing map(SOM)network.The model introduces a hybrid distance calculation method to measure inter-target distances and enhances the ant lion optimization algorithm through tent chaos sequences,adaptive tent chaos search,tournament selection,and logistic chaos sequences.Aggregation accuracy is evaluated using minimum quantization error and confidence value for the SOM neural network.The model is resolved using SATC-ALO and SOM independently,with experiments demonstrating that SOM achieves fast and accurate grouping,while SATC-ALO offers higher precision but requires longer computational runtime,making it more suitable for hybrid approaches.Both methods are validated as practical solutions for force aggregation tasks.
