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

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. T...

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Vydáno v:Mathematics (Basel) Ročník 7; číslo 2; s. 148
Hlavní autoři: Sun, Ying, Gao, Yuelin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 04.02.2019
Témata:
Algorithms
Archives & records
Convergence
Elitism
Food science
Gaussian mutation
Genetic algorithms
Heuristic methods
improved learning strategy
Machine learning
multi-objective optimization problems
Multiple objective analysis
Objectives
Optimization algorithms
Particle swarm optimization
particle swarm optimization (PSO)
Population
ISSN:2227-7390, 2227-7390
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:2227-7390
2227-7390
DOI:10.3390/math7020148