南京大学学报(自然科学版) ›› 2011, Vol. 47 ›› Issue (5): 504514.
刘建华**,杨荣华,孙水华
Liu Jian Hua ,Yank Rong Hua,Sun Shui Hua
摘要: 粒子群算法(Particle Swarm Optimization, PSO)主要用优化计算实值的连续性问题,而离散二进制粒子群算法(Binary Particle Swarm Optimization, BPSO)则用来优化离散空间问题,它扩展了
PSO算法的应用,现己]’一泛应用到各种离散优化问题计算中,但目前对BPS()算法的理论分析研究还很少,难以指导算法性能.木文从位改变概率和遗传算法的模式定理两方面对BPS()进行分析.分析得出,
BPSO算法具有很强全局搜索能力,但不能收敛于粒子的全局最优位置,而且随着算法迭代运行,BPSO的随机性越来越强,缺乏后期的局部搜索能力.木文利用基准的函数,通过仿真实验计算,验证木文的分
析结果.基于分析的结果,木文提出BPS()的改进方法,新方法采用新的概率映射函数和混合遗传算法的方法.通过对基准函数的仿真试验,验证了改进方法的有效性.
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