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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Published in:	The Artificial intelligence review Vol. 57; no. 5; p. 127
Main Authors:	Jia, Heming, Zhou, Xuelian, Zhang, Jinrui, Abualigah, Laith, Yildiz, Ali Riza, Hussien, Abdelazim G.
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer Netherlands 01.05.2024 Springer Springer Nature B.V
Subjects:	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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Abstract	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
AbstractList	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 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 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.
ArticleNumber	127
Audience	Academic
Author	Zhang, Jinrui Hussien, Abdelazim G. Abualigah, Laith Zhou, Xuelian Yildiz, Ali Riza Jia, Heming
Author_xml	– sequence: 1 givenname: Heming surname: Jia fullname: Jia, Heming email: jiaheming@fjsmu.edu.cn organization: School of Information Engineering, Sanming University – sequence: 2 givenname: Xuelian surname: Zhou fullname: Zhou, Xuelian organization: School of Information Engineering, Sanming University – sequence: 3 givenname: Jinrui surname: Zhang fullname: Zhang, Jinrui organization: School of Information Engineering, Sanming University – sequence: 4 givenname: Laith surname: Abualigah fullname: Abualigah, Laith organization: Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, MEU Research Unit, Middle East University – sequence: 5 givenname: Ali Riza surname: Yildiz fullname: Yildiz, Ali Riza organization: Department of Mechanical Engineering, Bursa Uludağ University – sequence: 6 givenname: Abdelazim G. surname: Hussien fullname: Hussien, Abdelazim G. organization: Department of Computer and Information Science, Linköping University
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Snippet	Crayfish Optimization Algorithm (COA) is innovative and easy to implement, but the crayfish search efficiency decreases in the later stage of the algorithm,...
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SubjectTerms	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
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Title	Modified crayfish optimization algorithm for solving multiple engineering application problems
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