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