摘要
为提升标准粒子群算法在求解多峰复杂问题时收敛速度慢和极易陷入局部最优解等缺点,提出一种基于球形坐标的分类学习策略粒子群算法(CLPSO-HC)。该算法给出种群运行较差粒子的确定方法,将运行较差的粒子进行分类,并对每类粒子给出相应的学习策略,保证种群跳出局部最优解的能力。为减少外界扰动,将粒子速度和位置的更新在球形坐标中进行,提升了种群向最优解飞行的概率。对三个典型测试函数进行仿真实验,所得结果表明CLPSO-HC相比其他几种算法有较好的收敛性。因此,CLPSO-HC可以作为求解复杂多峰问题的有效算法。
In order to deal with the problems of the slow convergence and easily converging to local optima, this paper pro- posed a classification learning PSO based on byperspherical coordinates. It presented the method of determination of poor per- formance particle, and divided the swarm into three parts where introduced three learning strategies to improve the swarm to es- cape from local optima. Additionally, to decrease outside disturbance, it updated the particle positions and velocities in hyper- spherical coordinate system, which improved the probability flying to the optimal solution. It conducted the simulation experi- ments of three typical functions, and the results show the effectiveness of the proposed algorithm compared with other algo- rithms. Conseauentlv. CLPSO-HC can be used as an effective algorithm to solve complex multimodal problems.
出处
《计算机应用研究》
CSCD
北大核心
2013年第8期2273-2275,共3页
Application Research of Computers
基金
中国博士后基金资助项目(2012M520936)
上海市博士后基金资助项目(12R21416000)
贵州省科学技术基金资助项目(黔科合J字LKZS[2012]01号
[2012]2340号
LKZS[2012]10号)
合肥师范学院博士基金资助项目(2012BSJJ19)
合肥师范学院基础教育研究课题(13ZYJ007)
贵州省高校优秀科技创新人才支持计划基金资助项目(黔教合KY[2012]104号)
关键词
粒子群优化
多峰问题
笛卡尔坐标
球形坐标
particle swarm optimizer（PSO）
multimodal problem
Cartesian coordinate
hyperspherical coordinates
