School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, China.
School of Mathematics, Zunyi Normal University, Zunyi 563002, China.
Comput Intell Neurosci. 2021 Nov 6;2021:6440338. doi: 10.1155/2021/6440338. eCollection 2021.
The optimization problems are taking place at all times in actual lives. They are divided into single objective problems and multiobjective problems. Single objective optimization has only one objective function, while multiobjective optimization has multiple objective functions that generate the Pareto set. Therefore, to solve multiobjective problems is a challenging task. A multiobjective particle swarm optimization, which combined cosine distance measurement mechanism and novel game strategy, has been proposed in this article. The cosine distance measurement mechanism was adopted to update Pareto optimal set in the external archive. At the same time, the candidate set was established so that Pareto optimal set deleted from the external archive could be effectively replaced, which helped to maintain the size of the external archive and improved the convergence and diversity of the swarm. In order to strengthen the selection pressure of leader, this article combined with the game update mechanism, and a global leader selection strategy that integrates the game strategy including the cosine distance mechanism was proposed. In addition, mutation was used to maintain the diversity of the swarm and prevent the swarm from prematurely converging to the true Pareto front. The performance of the proposed competitive multiobjective particle swarm optimizer was verified by benchmark comparisons with several state-of-the-art multiobjective optimizer, including seven multiobjective particle swarm optimization algorithms and seven multiobjective evolutionary algorithms. Experimental results demonstrate the promising performance of the proposed algorithm in terms of optimization quality.
优化问题在实际生活中无时无刻不在发生。它们分为单目标问题和多目标问题。单目标优化只有一个目标函数，而多目标优化有多个目标函数，这些目标函数生成了 Pareto 集。因此，解决多目标问题是一项具有挑战性的任务。本文提出了一种结合余弦距离度量机制和新颖博弈策略的多目标粒子群优化算法。该算法采用余弦距离度量机制来更新外部档案中的 Pareto 最优集。同时，建立了候选集，以便有效地替换从外部档案中删除的 Pareto 最优集，这有助于保持外部档案的大小，并提高了群体的收敛性和多样性。为了增强领导者的选择压力，本文结合博弈更新机制，提出了一种包含余弦距离机制的博弈策略的全局领导者选择策略。此外，通过变异来保持群体的多样性，防止群体过早收敛到真实的 Pareto 前沿。通过与几种最先进的多目标优化器进行基准比较，验证了所提出的竞争多目标粒子群优化器的性能，包括七种多目标粒子群优化算法和七种多目标进化算法。实验结果表明，该算法在优化质量方面具有良好的性能。
