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

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.