Mashru Nikunj, Kalita Kanak, Čepová Lenka, Patel Pinank, Jangir Pradeep
Department of Mechanical Engineering, Marwadi University, Rajkot, 360003, India.
Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, 600062, India.
Sci Rep. 2025 Apr 12;15(1):12690. doi: 10.1038/s41598-025-96901-y.
Balancing diversity and convergence among solutions in many-objective optimization is challenging, particularly in high-dimensional spaces with conflicting objectives. This paper presents the Many-Objective Marine Predator Algorithm (MaOMPA), an adaptation of the Marine Predators Algorithm (MPA) specifically enhanced for many-objective optimization tasks. MaOMPA integrates an elitist, non-dominated sorting and crowding distance mechanism to maintain a well-distributed set of solutions on the Pareto front. MaOMPA improves upon traditional metaheuristic methods by achieving a robust balance between exploration and exploitation using the predator-prey interaction model. The algorithm underwent evaluation on various benchmarks together with complex real-world engineering problems where it showed superior outcomes when compared against state-of-the-art generational distance and hypervolume and coverage metrics. Engineers and researchers can use MaOMPA as an effective reliable tool to address complex optimization scenarios in engineering design. The MaOMPA source code is available at https://github.com/kanak02/MaOMPA .
在多目标优化中平衡解决方案的多样性和收敛性具有挑战性，尤其是在具有冲突目标的高维空间中。本文提出了多目标海洋捕食者算法（MaOMPA），它是对海洋捕食者算法（MPA）的一种改进，专门针对多目标优化任务进行了增强。MaOMPA集成了精英、非支配排序和拥挤距离机制，以在帕累托前沿上保持一组分布良好的解决方案。MaOMPA通过使用捕食者 - 猎物相互作用模型在探索和利用之间实现稳健平衡，改进了传统的元启发式方法。该算法在各种基准测试以及复杂的实际工程问题上进行了评估，与最新的世代距离、超体积和覆盖率指标相比，它显示出了卓越的结果。工程师和研究人员可以将MaOMPA用作解决工程设计中复杂优化场景的有效可靠工具。MaOMPA的源代码可在https://github.com/kanak02/MaOMPA上获取。
