Abstract
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,which 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.
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