Modified crayfish optimization algorithm for solving multiple engineering application problems

Crayfish Optimization Algorithm (COA) is innovative and easy to implement, but the crayfish search efficiency decreases in the later stage of the algorithm, and the algorithm is easy to fall into local optimum. To solve these problems, this paper proposes an modified crayfish optimization algorithm...

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Veröffentlicht in:The Artificial intelligence review Jg. 57; H. 5; S. 127
Hauptverfasser: Jia, Heming, Zhou, Xuelian, Zhang, Jinrui, Abualigah, Laith, Yildiz, Ali Riza, Hussien, Abdelazim G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.05.2024
Springer
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Computer Science
Constraints
Engineering
Experiments
Feature selection
Habits
Hostility
Learning
Learning strategies
Mathematical optimization
Natural environment
Optimization
Optimization algorithms
Performance enhancement
Performance evaluation
Renewal
Water quality
High dimensional feature selection
Environmental updating mechanism
Constrained engineering design problems
Crayfish Optimization Algorithm
Ghost opposition-based learning strategy
Global optimization problem
ISSN:1573-7462, 0269-2821, 1573-7462
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Zusammenfassung:Crayfish Optimization Algorithm (COA) is innovative and easy to implement, but the crayfish search efficiency decreases in the later stage of the algorithm, and the algorithm is easy to fall into local optimum. To solve these problems, this paper proposes an modified crayfish optimization algorithm (MCOA). Based on the survival habits of crayfish, MCOA proposes an environmental renewal mechanism that uses water quality factors to guide crayfish to seek a better environment. In addition, integrating a learning strategy based on ghost antagonism into MCOA enhances its ability to evade local optimality. To evaluate the performance of MCOA, tests were performed using the IEEE CEC2020 benchmark function and experiments were conducted using four constraint engineering problems and feature selection problems. For constrained engineering problems, MCOA is improved by 11.16%, 1.46%, 0.08% and 0.24%, respectively, compared with COA. For feature selection problems, the average fitness value and accuracy are improved by 55.23% and 10.85%, respectively. MCOA shows better optimization performance in solving complex spatial and practical application problems. The combination of the environment updating mechanism and the learning strategy based on ghost antagonism significantly improves the performance of MCOA. This discovery has important implications for the development of the field of optimization. Graphical Abstract
Bibliographie:ObjectType-Article-1
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ISSN:1573-7462
0269-2821
1573-7462
DOI:10.1007/s10462-024-10738-x