改进的约束多目标粒子群算法

摘要

An improved Multiple Objective Particle Swarm Optimization MOPSO) algorithm for solving constrained multi-objective optimization problems (CMOPSO) was proposed based on the analysis of the characteristics of the multi-objective search space. A processing method taking dynamic e unfeasible degree allowable constraint dominance relation as the main constraint was brought forward in this paper, whieh aimed to improve the algorithm's ability of edge searching and crossing unconnected feasible regions. A simple density measuring method was put forward for external archive maintenance, which intended to improve the efficiency of the algorithm. A new global guide selection strategy was put forward, which brought better convergence and diversity to the algorithm. The computer simulation results show that the CMOPSO algorithm can find a sufficient number of Pareto optimal solutions that have better distribution, uniformity, and approachability.%在约束优化问题搜索空间分析的基础上提出了一种改进的约束多目标粒子群算法(CMOPSO).提出一种动态ε不可行度许可约束支配关系作为主要约束的处理方法,提高了算法的边缘搜索能力和跨越非联通可行区域的能力.设计了一种新的密集距离度量方法用于外部档案维护,提高了算法的效率；提出了新的全局向导选取策略,使算法获得了更好的收敛性和多样性.数值仿真实验结果表明约束多目标粒子群算法算法可得到分布性、均匀性及逼近性都较好的Pareto最优解.