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.

Index Terms—Multi-objective optimization, logistic mapping, C-MOPSO, crossover operator, Pareto optimal.

Liansong Xu is with the School of Mathematics and Information, West Normal University, China (e-mail: 654261644@ qq.com).
Dazhi Pan was with the School of Mathematics and Information, West Normal University, China(e-mail: pdzzj@126.com).

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Cite: Liansong Xu and Dazhi Pan, "Multi-objective Optimization Based on Chaotic Particle Swarm Optimization," International Journal of Machine Learning and Computing vol. 8, no. 3, pp. 229-235, 2018.