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
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: email@example.com).
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.