Implementation of an Enhanced Crayfish Optimization Algorithm

This paper presents an enhanced crayfish optimization algorithm (ECOA). The ECOA includes four improvement strategies. Firstly, the Halton sequence was used to improve the population initialization of the crayfish optimization algorithm. Furthermore, the quasi opposition-based learning strategy is i...

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Vydáno v:Biomimetics (Basel, Switzerland) Ročník 9; číslo 6; s. 341
Hlavní autoři: Zhang, Yi, Liu, Pengtao, Li, Yanhong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 04.06.2024
MDPI
Témata:
Accuracy
Algorithms
Competition
crayfish optimization algorithm
Design engineering
Experiments
fish device aggregation effect
Food
Foraging behavior
Halton sequence
Heat
IEEE CEC2019
Linear programming
Optimization algorithms
Predation
quasi opposition-based learning
Temperature
Halton sequence
crayfish optimization algorithm
IEEE CEC2019
fish device aggregation effect
quasi opposition-based learning
ISSN:2313-7673, 2313-7673
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Abstract This paper presents an enhanced crayfish optimization algorithm (ECOA). The ECOA includes four improvement strategies. Firstly, the Halton sequence was used to improve the population initialization of the crayfish optimization algorithm. Furthermore, the quasi opposition-based learning strategy is introduced to generate the opposite solution of the population, increasing the algorithm’s searching ability. Thirdly, the elite factor guides the predation stage to avoid blindness in this stage. Finally, the fish aggregation device effect is introduced to increase the ability of the algorithm to jump out of the local optimal. This paper performed tests on the widely used IEEE CEC2019 test function set to verify the validity of the proposed ECOA method. The experimental results show that the proposed ECOA has a faster convergence speed, greater performance stability, and a stronger ability to jump out of local optimal compared with other popular algorithms. Finally, the ECOA was applied to two real-world engineering optimization problems, verifying its ability to solve practical optimization problems and its superiority compared to other algorithms.
AbstractList This paper presents an enhanced crayfish optimization algorithm (ECOA). The ECOA includes four improvement strategies. Firstly, the Halton sequence was used to improve the population initialization of the crayfish optimization algorithm. Furthermore, the quasi opposition-based learning strategy is introduced to generate the opposite solution of the population, increasing the algorithm’s searching ability. Thirdly, the elite factor guides the predation stage to avoid blindness in this stage. Finally, the fish aggregation device effect is introduced to increase the ability of the algorithm to jump out of the local optimal. This paper performed tests on the widely used IEEE CEC2019 test function set to verify the validity of the proposed ECOA method. The experimental results show that the proposed ECOA has a faster convergence speed, greater performance stability, and a stronger ability to jump out of local optimal compared with other popular algorithms. Finally, the ECOA was applied to two real-world engineering optimization problems, verifying its ability to solve practical optimization problems and its superiority compared to other algorithms.
This paper presents an enhanced crayfish optimization algorithm (ECOA). The ECOA includes four improvement strategies. Firstly, the Halton sequence was used to improve the population initialization of the crayfish optimization algorithm. Furthermore, the quasi opposition-based learning strategy is introduced to generate the opposite solution of the population, increasing the algorithm's searching ability. Thirdly, the elite factor guides the predation stage to avoid blindness in this stage. Finally, the fish aggregation device effect is introduced to increase the ability of the algorithm to jump out of the local optimal. This paper performed tests on the widely used IEEE CEC2019 test function set to verify the validity of the proposed ECOA method. The experimental results show that the proposed ECOA has a faster convergence speed, greater performance stability, and a stronger ability to jump out of local optimal compared with other popular algorithms. Finally, the ECOA was applied to two real-world engineering optimization problems, verifying its ability to solve practical optimization problems and its superiority compared to other algorithms.This paper presents an enhanced crayfish optimization algorithm (ECOA). The ECOA includes four improvement strategies. Firstly, the Halton sequence was used to improve the population initialization of the crayfish optimization algorithm. Furthermore, the quasi opposition-based learning strategy is introduced to generate the opposite solution of the population, increasing the algorithm's searching ability. Thirdly, the elite factor guides the predation stage to avoid blindness in this stage. Finally, the fish aggregation device effect is introduced to increase the ability of the algorithm to jump out of the local optimal. This paper performed tests on the widely used IEEE CEC2019 test function set to verify the validity of the proposed ECOA method. The experimental results show that the proposed ECOA has a faster convergence speed, greater performance stability, and a stronger ability to jump out of local optimal compared with other popular algorithms. Finally, the ECOA was applied to two real-world engineering optimization problems, verifying its ability to solve practical optimization problems and its superiority compared to other algorithms.
Author Li, Yanhong
Liu, Pengtao
Zhang, Yi
AuthorAffiliation 2 Jilin Provincial Department of Human Resources and Social Security, Changchun 130000, China
1 College of Electrical and Computer Science, Jilin Jianzhu University, Changchun 130000, China; lpt1203@hotmail.com
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/38921221$$D View this record in MEDLINE/PubMed
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Keywords Halton sequence
crayfish optimization algorithm
IEEE CEC2019
fish device aggregation effect
quasi opposition-based learning
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StartPage 341
SubjectTerms Accuracy
Algorithms
Competition
crayfish optimization algorithm
Design engineering
Experiments
fish device aggregation effect
Food
Foraging behavior
Halton sequence
Heat
IEEE CEC2019
Linear programming
Optimization algorithms
Predation
quasi opposition-based learning
Temperature
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