Improved marine predators algorithm for engineering design optimization problems

The Marine Predator Algorithm (MPA) has unique advantages as an important branch of population-based algorithms. However, it emerges more disadvantages gradually, such as traps to local optima, insufficient diversity, and premature convergence, when dealing with complex problems in practical industr...

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Vydáno v:Scientific reports Ročník 14; číslo 1; s. 13000 - 23
Hlavní autoři: Chun, Ye, Hua, Xu, Qi, Chen, Yao, Ye Xin
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 06.06.2024
Nature Publishing Group
Nature Portfolio
Témata:
639/166
639/705
Algorithms
Complex industrial engineering design problems
Design engineering
Design optimization
Humanities and Social Sciences
Improved marine predators algorithm
Industrial engineering
Marine ecosystems
multidisciplinary
Objective function
Predators
Science
Science (multidisciplinary)
Self-adaptive weight
Social discrimination learning
Social strategy
Social strategy
Improved marine predators algorithm
Self-adaptive weight
Complex industrial engineering design problems
ISSN:2045-2322, 2045-2322
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Shrnutí:The Marine Predator Algorithm (MPA) has unique advantages as an important branch of population-based algorithms. However, it emerges more disadvantages gradually, such as traps to local optima, insufficient diversity, and premature convergence, when dealing with complex problems in practical industrial engineering design applications. In response to these limitations, this paper proposes a novel Improved Marine Predator Algorithm (IMPA). By introducing an adaptive weight adjustment strategy and a dynamic social learning mechanism, this study significantly improves the encounter frequency and efficiency between predators and preys in marine ecosystems. The performance of the IMPA was evaluated through benchmark functions, CEC2021 suite problems, and engineering design problems, including welded beam design, tension/compression spring design, pressure vessel design, and three-bar design. The results indicate that the IMPA has achieved significant success in the optimization process over other methods, exhibiting excellent performance in both solving optimal parameter solutions and optimizing objective function values. The IMPA performs well in terms of accuracy and robustness, which also proves its efficiency in successfully solving complex industrial engineering design problems.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-63826-x