In this paper, a multi-objective version of the recently proposed marine predator algorithm (MPA) is presented, which is called the multi-objective marine predator algorithm (MOMPA). In this algorithm, an external archive component is introduced to store the non dominated Pareto optimal solutions found so far. Based on the elite selection method, a top predator selection mechanism is proposed, which selects the effective solutions from the archive as the top predators to simulate the predator’s foraging behavior. The CEC2019 multi-modal multi-objective benchmark functions are utilized to evaluate the performance of the proposed algorithm and compared with nine state-of-the-art multi-objective meta-heuristics algorithms. In addition, seven multi-objective engineering design problems (car side impact problem, gear train design problem, welded beam design problem, disk brake design problem, two bar truss design problem, spring design problem and cantilever beam design problem) are used to further verify the effectiveness of the proposed algorithm. The results demonstrate that the proposed MOMPA algorithm not only provides very competitive results but also outperforms other algorithms.

在本文中，提出了最近提出的海洋捕食者算法（MPA）的多目标版本，称为多目标海洋捕食者算法（MOMPA）。在该算法中，引入了一个外部存档组件来存储目前找到的非支配帕累托最优解。基于精英选择方法，提出了顶级捕食者选择机制，从档案中选择有效解作为顶级捕食者，模拟捕食者的觅食行为。CEC2019 多模态多目标基准函数用于评估所提出算法的性能，并与九种最先进的多目标元启发式算法进行比较。另外，7个多目标工程设计问题(汽车侧面碰撞问题、齿轮系设计问题、焊接梁设计问题、盘式制动器设计问题、两杆桁架设计问题、弹簧设计问题和悬臂梁设计问题)被用来进一步验证所提出算法的有效性。结果表明，所提出的 MOMPA 算法不仅提供了非常有竞争力的结果，而且优于其他算法。