Crayfish optimization algorithm

This paper proposes a meta heuristic optimization algorithm, called Crayfish Optimization Algorithm (COA), which simulates crayfish’s summer resort behavior, competition behavior and foraging behavior. The three behaviors are divided into three different stages to balance the exploration and exploit...

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Vydáno v:The Artificial intelligence review Ročník 56; číslo Suppl 2; s. 1919 - 1979
Hlavní autoři: Jia, Heming, Rao, Honghua, Wen, Changsheng, Mirjalili, Seyedali
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.11.2023
Springer
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Behavior
Benchmarks
Competition
Computer Science
Education
Experiments
Exploitation
Food
Food consumption
Food intake
Foraging behavior
Global optimization
Heuristic
Hunter-gatherers
Mathematical optimization
Methods
Optimization
Optimization algorithms
Original Paper
Randomness
Resorts
Resorts & spas
Source code
Summer
Competition stage
Crayfish optimization algorithm
Summer resort stage
Foraging stage
ISSN:0269-2821, 1573-7462
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Shrnutí:This paper proposes a meta heuristic optimization algorithm, called Crayfish Optimization Algorithm (COA), which simulates crayfish’s summer resort behavior, competition behavior and foraging behavior. The three behaviors are divided into three different stages to balance the exploration and exploitation of algorithm. The three stages are summer resort stage, competition stage and foraging stage. The summer resort stage represents the exploration stage of the COA. The competition stage and foraging stage represent the exploitation stage of the COA. Exploration and exploitation of COA are regulated by temperature. When the temperature is too high, crayfish will enter the cave for summer vacation or compete for the same cave. When the temperature is appropriate, crayfish have different foraging behaviors according to the size of food. Among them, the amount of food eaten by crayfish is related to food intake. Through temperature regulate exploration and exploitation process in COA, the COA has higher randomness and global optimization effect. To verify the optimization effect of COA, in the experimental part, 23 standard benchmark functions and CEC2014 benchmark functions are used to test, and 9 algorithms are selected for comparative experiments. The experimental results show that COA can balance the exploration and exploitation, and achieve good optimization effect. Finally, the COA is tested in five engineering problems, and finally achieves better results. The source code website for COA is https://github.com/rao12138/COA-s-code .
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-023-10567-4