Study of Modified Particle Swarm Optimization Algorithm Based on Adaptive Mutation Probability

Abstract

Abstract: Mutation operator is an effective method to solve the premature of particle swarm optimization. According to the characteristic of population diversity, a modified particle swarm optimization based on adaptive mutation probability and hybrid mutation strategy was proposed. Aggregation degree was introduced to adjust the mutation probability of each generation, and a hybrid Gaussian and Cauchy mutation based on the global-best position and an adaptive wavelet mutation based on the worst personal-best position were carried out. The simulation of the comparisons with other particle swarm optimizations with mutation operator on matlab was proposed. The results demonstrate that the proposed algorithm can obtain higher accuracy solution and have better performance.

Key words: particle swarm optimization, mutation probability, adaptive, hybrid mutation

CLC Number:

TP18

Huang Song, Tian Na, Ji Zhicheng. Study of Modified Particle Swarm Optimization Algorithm Based on Adaptive Mutation Probability[J]. Journal of System Simulation, 2016, 28(4): 874-879.

References

[1] 任子晖, 王坚. 一种动态改变惯性权重的自适应粒子群算法[J]. 计算机科学, 2009, 36(2): 227-229.
[2] 赵远东, 方正华. 带有权重函数学习因子的粒子群算法[J]. 计算机应用, 2013, 33(8): 2265-2268.
[3] Zhang G, Shao X, Li P, et al.An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem[J]. Computers and Industrial Engineering (S0360-8352), 2009, 56(4): 1309-1318.
[4] Xia L, Chu J, Geng Z.A New Multi-Swarms Competitive Particle Swarm Optimization Algorithm[M]. Advances in Information Technology and Industry Applications (S1876-1100). Germany: Springer Berlin Heidelberg, 2012: 133-140.
[5] 付绍昌, 黄辉先, 肖业伟, 等. 自适应变异粒子群算法在交通控制中的应用[J]. 系统仿真学报, 2007, 19(7): 1562-1564.
(Fu S, Huang H, Xiao Y, et al.Application of Adaptive Mutation-particle Swarm Optimization Algorithm in Traffic Control[J]. Journal of System Simulation (S1004-731X), 2007, 19(7): 1562-1564.)
[6] 阳春华, 谷丽姗, 桂卫华. 自适应变异的粒子群优化算法[J]. 计算机工程, 2008, 34(16): 188-190.
[7] 刘炳全, 黄崇超. 具有追尾行为的自适应变异粒子群算法[J]. 计算机工程与应用, 2009, 44(30): 74-76.
[8] 吴定海, 张培林, 李胜, 等. 基于混沌变异的自适应双粒子群优化[J]. 控制与决策, 2011, 26(7): 1083-1086.
[9] 陶新民, 付丹丹, 刘玉, 等. 双尺度变异离散粒子群算法求解背包问题[J]. 系统仿真学报, 2013, 25(1): 12-17.
(Tao Xinmin, Fu Dandan, Liu Yu, et al.Discrete Particle Swarm Optimization Algorithm Based on Double-scale Mutation for Knapsack Problems[J]. Journal of System Simulation (S1004-731X), 2013, 25(1): 12-17.)
[10] 李怀俊, 谢小鹏, 肖心远. 基于粒子熵的参数自适应变异 PSO 算法研究[J]. 计算机工程与应用, 2014, 50(19): 27-31.
[11] 王铁君, 邬月春. 基于混沌自适应变异粒子群算法的铁路空车调配[J]. 计算机应用研究, 2011, 28(4): 1276-1278.
[12] 向毅, 钟育彬. 自适应阶段变异量子粒子群优化算法研究[J]. 计算机应用研究, 2012, 29(6): 2035-2039.
[13] Xie X F, Zhang W J, Yang Z L.A dissipative particle swarm optimization[C]// Proceedings of the IEEE International Conference on Evolutionary Computation, Honolulu, USA. USA: IEEE, 2002: 1456-1461.
[14] Ratnaweera A, Halgamuge S K.Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J]. IEEE Transaction on Evolutionary Computation (S1089-778X), 2004, 8(3): 240-255.
[15] Zhang W J, Xie X F.DEPSO: Hybrid particle swarm with differential evolution operater[C]// Proceedings of IEEE International Conference on System. Man and Cybernetics, Washington D C, USA. USA: IEEE, 2003: 3816-3821.