一种改进的约束优化粒子群算法

摘要

Using non-stationary multi-stage assignment penalty function method to deal with the constraint conditions, this paper proposed a novel constrained optimization particle swarm optimization algorithm. It used chaotic sequences in the initialization of the evolutionary population. In the process of population evolution, the proposed algorithm selected the best population individual for local search to speed up the convergence rate of the algorithm. It maintained the population diversity through dimension mutation method. Numerical experiment results show that it is an effective algorithm.%提出一种新的约束优化粒子群算法.该算法采用非固定多段映射罚函数法处理约束条件.在进化过程中,利用混沌序列初始化种群,选取最优粒子进行局部一维搜索,增强了在最优点附近的局部搜索能力,以加快算法的收敛速度；引入维变异方法保持种群的多样性.数值实验结果表明了该算法的有效性.