A Multi-Objective Particle Swarm Optimization Algorithm Based on Gaussian Mutation and an Improved Learning Strategy

Ying Sun and Yuelin Gao
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Ying Sun: School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
Yuelin Gao: School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China

Mathematics, 2019, vol. 7, issue 2, 1-16

Abstract: Obtaining high convergence and uniform distributions remains a major challenge in most metaheuristic multi-objective optimization problems. In this article, a novel multi-objective particle swarm optimization (PSO) algorithm is proposed based on Gaussian mutation and an improved learning strategy. The approach adopts a Gaussian mutation strategy to improve the uniformity of external archives and current populations. To improve the global optimal solution, different learning strategies are proposed for non-dominated and dominated solutions. An indicator is presented to measure the distribution width of the non-dominated solution set, which is produced by various algorithms. Experiments were performed using eight benchmark test functions. The results illustrate that the multi-objective improved PSO algorithm (MOIPSO) yields better convergence and distributions than the other two algorithms, and the distance width indicator is reasonable and effective.

Keywords: multi-objective optimization problems; particle swarm optimization (PSO); Gaussian mutation; improved learning strategy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
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