基于多种群的改进粒子群算法多模态优化  

Enhanced multi-species-based particle swarm optimization for multi-modal function

作　　者：谢红侠[1] 马晓伟[2] 陈晓晓[2] 邢强[2] 

机构地区：[1]中国矿业大学计算机科学与技术学院,江苏徐州221116 [2]中国矿业大学信息与电气工程学院,江苏徐州221008

出　　处：《计算机应用》2016年第9期2516-2520,共5页journal of Computer Applications

基　　金：江苏省自然科学基金资助项目(BK2013 0205)~~

摘　　要：针对多模态函数寻优过程中开发与探索能力难以平衡的问题,提出一种基于多种群的改进粒子群算法(EMSPSO)。该算法在基于种群的粒子群算法(SPSO)的基础上改进了种群生成策略,通过在个体最优值中选择种子,将粒子群分为若干独立进化的种群,增强了算法收敛的稳定性;为了提高粒子的利用率、算法的全局搜索能力和搜索效率,引入冗余粒子重新初始化策略;同时为了防止算法在寻优的过程中遗漏适应度较优的极值点,对速度更新公式进行改进,使算法的开发与探索能力得到了有效的均衡。最后选用6个典型的测试函数进行对比实验,实验结果表明,EMSPSO具有较高的多模态寻优成功率与较优的全局极值搜索性能。It is difficult to balance local development and global exploration in a multi-modal function optimization process, therefore, an Enhanced Multi-Species-based Particle Swarm Optimization （EMSPSO） was proposed. An improved multi-species evolution strategy was introduced to Species-based Particle Swarm Optimization （SPSO）. Several species which evolved independently were established by selecting seed in the individual optimal values to improve the stability of algorithm convergence. A redundant particle reinitialization strategy was introduced to the algorithm in order to improve the utilization of the particles, and enhance global search capability and search efficiency of the algorithm. Meanwhile, in order to prevent missing optimal extreme points in the optimization process, the rate update formula was also improved to effectively balance the local development and global exploration capability of the algorithm. Finally, six typical test functions were selected to test the performance of EMSPSO. The experimental resuhs show that, EMSPSO has high muhi-modal optimization success rate and optimal performance of global extremum search.

关 键 词：多模态函数优化 粒子群算法 小生境技术 多种群 冗余粒子 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]