演化信息协助的动态协同随机漂移粒子群优化算法  

Dynamic cooperative random drift particle swarm optimization algorithm assisted by evolution information

作　　者：赵吉 程成[3] ZHAO Ji;CHENG Cheng(Wuxi Research Center for Environmental Science and Engineering,Wuxi Jiangsu 214153,China;School of IoT Engineering,Jiangnan University,Wuxi Jiangsu 214122,China;China Ship Scientific Research Center,Wuxi Jiangsu 214082,China)

机构地区：[1]无锡环境科学与工程研究中心,江苏无锡214153 [2]江南大学物联网工程学院,江苏无锡214122 [3]中国船舶科学研究中心,江苏无锡214082

出　　处：《计算机应用》2020年第11期3119-3126,共8页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61672263);江苏省高校“青蓝工程”中青年学术带头人项目(2019);无锡环境科学与工程研究中心中青年学术带头人项目(2018)。

摘　　要：为了改善随机漂移粒子群算法的群体多样性,通过演化信息的协助,提出动态协同随机漂移粒子群优化(CRDPSO)算法。利用上下文粒子的向量信息,粒子之间的动态协作增加了种群多样性,这有助于提高群体的搜索能力,并使整个群体协同搜索全局最优值。同时在演化过程中的每次迭代,利用二维空间分割树结构来存储算法中的估计解的位置和适应度值,从而实现快速适应度函数逼近。由于适应度函数逼近增强了变异策略,因此变异是自适应且无参数的。通过典型测试函数将CRDPSO算法和差分进化算法(DE)、协方差矩阵适应进化策略算法(CMA-ES)、非重复访问遗传算法(cNrGA)以及三种改进的量子行为粒子群算法(QPSO)进行比较。实验结果表明,不管是对于单峰还是多峰测试函数,CRDPSO的性能均是最优的,证明了该算法的有效性。A dynamic Cooperative Random Drift Particle Swarm Optimization(CRDPSO)algorithm assisted by evolution information was proposed in order to improve the population diversity of random drift particle swarm optimization.By using the vector information of context particles,the population diversity was increased by the dynamic cooperation between the particles,to improve the search ability of the swarm and make the whole swarm cooperatively search for the global optimum.At the same time,at each iteration during evolution,the positions and the fitness values of the evaluated solutions in the algorithm were stored by a binary space partitioning tree structure archive,which led to the fast fitness function approximation.The mutation was adaptive and nonparametric because of the fitness function approximation enhanced the mutation strategy.CRDPSO algorithm was compared with Differential Evolution(DE),Covariance Matrix Adaptation Evolution Strategy(CMA-ES),continuous Non-revisiting Genetic Algorithm(cNrGA)and three improved Quantum-behaved Particle Swarm Optimization(QPSO)algorithms through a series of standard test functions.Experimental results show that the performance of CRDPSO is optimal for both unimodal and multimodal test functions,which proves the effectiveness of the algorithm.

关 键 词：群体智能 动态协同进化 演化信息 自适应无参变异 二维空间分割 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]