具有协同寻优的蝙蝠萤火虫混合优化算法
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摘要
萤火虫算法存在着对于初始解分布的依赖性、后期收敛速度慢、易于停滞、早熟和求解精度低等缺陷。本研究在萤火虫算法引入蝙蝠种群在全局最优附近进行更加详细的局部搜索,以协助萤火虫种群进行寻优;并在寻优过程中加强蝙蝠种群与萤火虫种群的信息交互,协调寻优;最后对全局最优个体进行高斯扰动以增加种群的多样性,从而避免种群陷入局部最优解。通过使用6个常见的基准测试函数对该算法进行测试,并与其他3种算法(标准粒子群算法、蝙蝠算法、萤火虫算法)进行对比实验,结果表明该混合算法的总体性能优于其他3种算法。引入蝙蝠种群对萤火虫性能有较大提升,改善切实有效。
The improved firefly algorithm has defects such as dependence on the initial solution distribution,slow convergence in the later stage,easy stagnation,early maturity and low accuracy.This algorithm introduces the bat population to do a more detailed local search near the global optimum to help the firefly population to optimize.In the process of optimization,the information interaction between the bat population and the firefly population is enhanced,and the optimization is coordinated.Finally,Gaussian perturbation is applied to the global optimum individuals to increase the diversity of the population and avoid the population falling into the local optimum solution.The algorithm was tested by using six benchmark functions and compared with the other three algorithms(standard particle swarm algorithm,bat algorithm,firefly algorithm).The results show the overall performance of this hybrid algorithm is better than the other three algorithms.The introduction of the bat population has greatly improved the performance of firefly,and the improvement is effective.
The improved firefly algorithm has defects such as dependence on the initial solution distribution,slow convergence in the later stage,easy stagnation,early maturity and low accuracy.This algorithm introduces the bat population to do a more detailed local search near the global optimum to help the firefly population to optimize.In the process of optimization,the information interaction between the bat population and the firefly population is enhanced,and the optimization is coordinated.Finally,Gaussian perturbation is applied to the global optimum individuals to increase the diversity of the population and avoid the population falling into the local optimum solution.The algorithm was tested by using six benchmark functions and compared with the other three algorithms(standard particle swarm algorithm,bat algorithm,firefly algorithm).The results show the overall performance of this hybrid algorithm is better than the other three algorithms.The introduction of the bat population has greatly improved the performance of firefly,and the improvement is effective.
引文
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[12]左仲亮,郭星,李炜.一种改进的萤火虫算法[J].微电子学与计算机,2018,35(2):61-66.
[2]YANG X S.A new metaheuristic bat-inspired algorithm[M]//Nature inspired cooperative strategies for optimization.Berlin:Springer,2010:65-74.
[3]YANG X S.Nature-inspired metaheuristic algorithm[M].Beckington,UK:Luniver Press,2010.
[4]王艳,王秋萍,王晓峰.基于改进萤火虫算法求解旅行商问题[J].计算机系统应用,2018,27(8):219-225.
[5]LUCKASIK S,6)ZAK S.Firefly algorithm for continuous constrained optimization task[C]//ICCCI 2009:Proceedings of the First International Conference on computational collective intelligence,semantic web,social networks and multi-agent systems,LNCS5796.Berlin:Springer,2009:97-100.
[6]冯艳红,刘建芹,贺毅朝.基于混沌理论的动态种群萤火虫算法[J].计算机应用,2013,33(3):796-799,805.
[7]王吉权,王福林.萤火虫算法的改进分析及应用[J].计算机应用,2014,34(9):2552-2556.
[8]GANDOMI A H,YAN X S,TALATAHARI A H,et al.Firefly algorithm with chaos[J].Communications in Nonlinear Science and Numerical Simulation,2013,18(1):89-98.
[9]付强,葛洪伟,苏树智.引入萤火虫行为和Levy飞行的粒子群优化算法[J].计算机应用,2016,36(12):3298-3302,3310.
[10]郝晓莹,贺兴时,薛菁菁.一种粒子群-萤火虫算法的参数优化方法[J].西安工程大学学报,2017,31(5):695-700.
[11]杜艳艳,刘升.一种改进的自适应混合型蝙蝠算法[J].微电子学与计算机,2018,35(6):135-140.
[12]左仲亮,郭星,李炜.一种改进的萤火虫算法[J].微电子学与计算机,2018,35(2):61-66.