一种基于混沌动态权重粒子群优化算法
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摘要
粒子群优化算法(PSO)是一种自然启发式的全局优化算法,它受到鸟群中个体社会行为的启发.PSO算法很容易实现并且对许多现实世界的优化问题表现出良好的性能.然而,PSO算法存在过早收敛并且容易陷入局部极值点,为了克服这些缺陷,提出了一种基于混沌动态权重粒子群优化算法(CPSO).在CPSO算法中,引入混沌映射和动态权重来修改搜索过程,动态权重被定义为适应度值的函数.通过选取10个著名的经典基准测试函数来验证CPSO算法的搜索精度和性能.实验结果表明,CPSO算法的性能优于其它自然启发的优化算法和标准的PSO算法,所以提出的CPSO算法具有较好的搜索性能.
Particle swarm optimization algorithm(PSO)is a natural heuristic global optimization algorithm inspired by social behavior of individuals in bird flocks.The PSO algorithm is easy to implement and shows good performance for many real-world optimization problems.However,the PSO algorithm has the problem of premature convergence and is easy to fall into local extremum.To overcome these shortcomings,aparticle swarm optimization algorithm based on chaotic dynamic weight(CPSO)is proposed.In the CPSO algorithm,chaotic maps and dynamic weights are introduced to modify the search process,and dynamic weights are defined as functions of fitness values.The search accuracy and performance of the CPSO algorithm are verified by selecting 10 well-known classical benchmark functions.The experimental results show that the CPSO algorithm outperforms other nature-inspired optimization algorithms and the standard PSO algorithms,so the CPSO algorithm proposed in this paper has better search performance.
Particle swarm optimization algorithm(PSO)is a natural heuristic global optimization algorithm inspired by social behavior of individuals in bird flocks.The PSO algorithm is easy to implement and shows good performance for many real-world optimization problems.However,the PSO algorithm has the problem of premature convergence and is easy to fall into local extremum.To overcome these shortcomings,aparticle swarm optimization algorithm based on chaotic dynamic weight(CPSO)is proposed.In the CPSO algorithm,chaotic maps and dynamic weights are introduced to modify the search process,and dynamic weights are defined as functions of fitness values.The search accuracy and performance of the CPSO algorithm are verified by selecting 10 well-known classical benchmark functions.The experimental results show that the CPSO algorithm outperforms other nature-inspired optimization algorithms and the standard PSO algorithms,so the CPSO algorithm proposed in this paper has better search performance.
引文
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[2]胡旺,李志蜀.一种更简化而高效的粒子群优化算法[J].软件学报,2007,18(4):861-868.
[3]DU K L,SWAMY M N S.Search and Optimization by Metaheuristics[M].Birkhauser:Springer International Publishing,2016:153-173.
[4]MIRJALILI S.SCA:a sine cosine algorithm for solving optimization problems[J].Knowl Based Syst,2016,96:120-133.
[5]MIRJALILI S.Moth-flame optimization algorithm:A novel nature-inspired heuristic paradigm[J].Knowl Based Syst,2015,89:228-249.
[6]DU K L,SWAMY M N S.Ant colony optimization,in:Search and Optimization by Metaheuristics[M].Springer:Springer International Publishing,2016:191-199.
[7]YANG X S.Multiobjective firefly algorithm for continuous optimization[J].Eng Comput,2013,29(2):175-184.
[8]LI G,NIU P,MA Y,et al.Tuning extreme learning machine by an improved artificial bee colony to model and optimize the boiler efficiency[J].Knowl Based Syst,2014,67:278-289.
[9]SAREMI S,MIRJALILI S,LEWIS A.Biogeography-based optimization with chaos[J].Neural Comput Appl,2014,25(5):1077-1097.
[10]JU F Y,HONG W C.Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting[J].Appl Math Model,2013,37(23):9643-9651.
[11]ALATAS B.Chaotic harmony search algorithms[J].Appl Math Comput,2010,216(9):2687-2699.
[12]WANG G G,GUO L,GANDOMI A H,et al.Chaotic krill herd algorithm[J].Inf Sci,2014,274:17-34.
[13]EBENHART R.Kennedy,Particle swarm optimization,in:Proceeding IEEE Inter Conference on Neural Networks,4,Perth, Australia,Piscat-away[C]//In proceedings,1995:1942-1948.
[14]VIDAL T,CRAINIC T G,GENDREAU M,et al.A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows[J].Comput Oper Res,2013,40(1):475-489.
[15]CIVICIOGLU P,BESDOK E.A conceptual comparison of the Cuckoo-search,particle swarm optimization,differential evolution and artificial bee colony algorithms[J].Artif Intell Rev,2013(2):1-32.
[16]YANG X S,GANDOMI A H.Bat algorithm:a novel approach for global engineering optimization[J].Eng Comput,2012,29(5):464-483.
[17]胡建秀,曾建潮.二阶振荡微粒群算法[J].系统仿真学报,2007,19(5):997-999
[18]张超,李擎,王伟乾,等.基于自适应搜索的免疫粒子群算法[J].工程科学学报,2017,39(1):125-132.