Adaptive predator prey algorithm for many objective optimization

doi:10.1038/s41598-025-96901-y

Adaptive predator prey algorithm for many objective optimization

2025-04-12

0

OA

AI

DOI：10.1038/s41598-025-96901-y

原文链接

查看原文

摘要

En 中文

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.

Keyword:

Convergence

Diversity

Many-objective optimization

Metaheuristic algorithm

Information feedback mechanism

Marine predator algorithm

Adaptive predator prey algorithm for many objective optimization

摘要

AI总结

期刊

机构

引用论文