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• W2913367039 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." @default.
• W2913367039 created "2019-02-21" @default.
• W2913367039 creator A5046835504 @default.
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• W2913367039 date "2019-02-04" @default.
• W2913367039 modified "2023-10-10" @default.
• W2913367039 title "A Multi-Objective Particle Swarm Optimization Algorithm Based on Gaussian Mutation and an Improved Learning Strategy" @default.
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• W2913367039 doi "https://doi.org/10.3390/math7020148" @default.
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