Multi-objective Optimization Based on Chaotic Particle Swarm Optimization

Abstract

The multi-objective problem is particularly difficult in practical engineering applications, so more and more scholars have studied the problem to find the true Pareto optimal solution.In order to improve the convergence performance of multi-objective optimization algorithm and diversity, this paper proposes a multi-objective optimization algorithm based on chaos particle swarm optimization algorithm: using Logistic mapping sequences in solution in the particle swarm algorithm is updated; introducing the crossover operator of normal distribution to improve the diversity of the population; and using simplified mesh reduction and gene exchange to improve the performance of the algorithm.Compared with the MOPSO, NSGA-II and MOEA/D algorithms, it is shown that the proposed algorithm has good performance and can effectively solve the multi-objective optimization problem.