Adaptive predator prey algorithm for many objective optimization

. 2025 Apr 12 ; 15 (1) : 12690. [epub] 20250412

Jazyk angličtina Země Velká Británie, Anglie Médium electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid40221537

Grantová podpora
REFRESH - Research Excellence For REgion Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 European Union
Students Grant Competition SP2025/062 Czech Republic Ministry of Education, Youth and Sports and Faculty of Mechanical Engineering VŠB-TUO

Odkazy

PubMed 40221537
PubMed Central PMC11993708
DOI 10.1038/s41598-025-96901-y
PII: 10.1038/s41598-025-96901-y
Knihovny.cz E-zdroje

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 .

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