基于猫群思想的混合人工蜂群算法-《计算机技术与发展》

文章信息/Info

Author(s):: ZHAO Yu-xia; XU Xiao-zhong; HUANG Wei; MA Yan; School of Information,Mechanical and Electrical Engineering,Shanghai Normal University,Shanghai 200234,China

Keywords:: random new solution; thought of cat swarm optimization algorithm; tracking mode; artificial bee colony algorithm; functionoptimization

摘要:: 为了缓解基本人工蜂群算法后期种群多样性下降,易陷入局部最优,开采能力较差等问题,提出一种基于猫群思想的混合人工蜂群算法.提出基于随机新解引导的自适应搜索策略,结合多次高斯搜索机制,对雇佣蜂阶段进行优化;引入基于猫群思想的搜索过程,结合顺序模式分配方式,对较优解执行搜寻模式,对较差解执行优化后的跟踪模式;优化后的跟踪模式采用"位移"模型对解进行更新.对标准测试函数寻优,结果表明混合人工蜂群算法收敛精度更高,所需的迭代次数更少.得出结论:基于随机新解引导的自适应搜索策略能有效缓解算法易陷入局部最优的问题,基于猫群思想的搜索过程能有效提高算法的局部开采能力和全局搜索能力,混合人工蜂群算法具有更优秀的收敛性能.

Abstract:: In order to alleviate the problems of declining population diversity,falling into local optimum easily and poor mining for basicartificial bee colony algorithm in the later stage,we propose a hybrid artificial bee colony algorithm based on thought of cat swarm. Anadaptive search strategy based on random new solution guidance is proposed to optimize the employment bee stage with the repeatedGaussian search mechanism. The search process based on the thought of cat swarm is introduced to perform seeking mode for the bettersolution and optimized tracking mode for the worse solution combining with the sequential pattern allocation method. The optimizedtracking mode uses the “position” model to update the solution. The test of the standard functions shows that the hybrid artificial bee colony algorithm has higher convergence precision with fewer iterations. It is concluded that the adaptive search strategy which guided byrandom new solution can effectively alleviate the problem of falling into local optimum easily,the searching process based on the thoughtof cat swarm optimization algorithm can effectively improve the local mining and global search ability,and the hybrid artificial bee colonyalgorithm has better convergence performance