This paper proposes a novel optimization algorithm called the Multi-Objective Volleyball Premier League (MOVPL) algorithm for solving global optimization problems with multiple objective functions. The algorithm is inspired by the teams competing in a volleyball premier league. The strong point of this study lies in extending the multi-objective version of the Volleyball Premier League algorithm (VPL), which is recently used in such scientific researches, with incorporating the well-known approaches including archive set and leader selection strategy to obtain optimal solutions for a given problem with multiple contradicted objectives. To analyze the performance of the algorithm, ten multi-objective benchmark problems with complex objectives are solved and compared with two well-known multi-objective algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D). Computational experiments highlight that the MOVPL outperforms the two state-of-the-art algorithms on multi-objective benchmark problems. In addition, the MOVPL algorithm has provided promising results on well-known engineering design optimization problems.

提出了一种新颖的优化算法，称为多目标排球超级联赛（MOVPL）算法，用于解决具有多个目标函数的全局优化问题。该算法的灵感来自于排球超级联赛中的球队。这项研究的强项在于扩展排球超级联赛算法（VPL）的多目标版本，该算法最近在此类科学研究中使用，并结合了包括档案集和领导者选择策略在内的众所周知的方法来获得最佳选择。具有多个矛盾目标的给定问题的解决方案。为了分析该算法的性能，解决了十个具有复杂目标的多目标基准问题，并将其与两种著名的多目标算法进行了比较，即多目标粒子群算法（MOPSO）和基于分解的多目标进化算法（MOEA / D）。计算实验表明，在多目标基准问题上，MOVPL优于两种最新算法。此外，MOVPL算法在解决著名的工程设计优化问题上也提供了有希望的结果。