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基于非均匀变异和多阶段扰动的粒子群优化算法
  • 摘要

    该文提出一种基于非均匀变异和多阶段扰动的粒子群优化算法,并对算法的搜索性能进行了一般性分析。首先,在算法执行的不同阶段利用对当前最优解施加大小不同的邻域扰动操作,很好地增加了群体多样性,提高了跳出局部陷阱的概率,同时加强了对当前最优解邻域内的精细搜索;其次,在粒子群优化算法中引入非均匀变异运算,并依据非均匀变异运算规律适应性地调整解向量的搜索步长。算法性能分析表明,本算法较好地兼顾了群体优化算法的多样性和精英学习强度之间的平衡问题。数值实验上,首先用12个经典测试函数,验证该文提出的几种新措施的有效性与互助性;其次,针对30维和50维的CEC2005测试函数集,所提算法NmP3PSO与经典算法wFIPS、CLPSO和OLPSO做了大量的仿真实验,结果表明该文提出的算法表现出富有竞争力的性能和稳定性。

  • 作者

    赵新超  刘国莅  刘虎球  赵国帅 

  • 作者单位

    北京邮电大学理学院 北京 100876/北京大学信息科学技术学院 北京 100871/清华大学计算机科学与技术系 北京 100084/北京邮电大学网络技术研究院 北京 100876

  • 刊期

    2014年9期 ISTIC EI PKU

  • 关键词

    粒子群优化  非均匀变异  多阶段扰动  群体多样性 

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