In order to solve the shortcomings of particle swarm optimization (PSO) in solving multiobjective optimization problems, an improved multiobjective particle swarm optimization (IMOPSO) algorithm is proposed. In this study, the competitive strategy was introduced into the construction process of Pareto external archives to speed up the search process of nondominated solutions, thereby increasing the speed of the establishment of Pareto external archives. In addition, the descending order of crowding distance method is used to limit the size of external archives and dynamically adjust particle parameters; in order to solve the problem of insufficient population diversity in the later stage of algorithm iteration, time-varying Gaussian mutation strategy is used to mutate the particles in external archives to improve diversity. The simulation experiment results show that the improved algorithm has better convergence and stability than the other compared algorithms.

中文翻译：

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基于竞争机制和高斯变异的多目标粒子群优化算法

为了解决粒子群算法在求解多目标优化问题上的不足，提出了一种改进的多目标粒子群算法。在这项研究中，竞争策略被引入到Pareto外部档案的构建过程中，以加快非支配解决方案的搜索过程，从而提高了Pareto外部档案的建立速度。此外，采用拥挤距离降序方法来限制外部档案的大小并动态调整粒子参数。为了解决算法迭代后期的种群多样性不足问题，采用时变高斯变异策略对外部档案中的粒子进行变异，以提高多样性。