Journal Article

Crayfish optimization algorithm

2023

Overview

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.

• Publisher: Springer Nature B.V
• Subject: Algorithms / Behavior / Benchmarks / Competition / Experiments / Exploitation / Food / Food consumption / Food intake / Foraging behavior / Global optimization / Heuristic / Hunter-gatherers / Optimization / Optimization algorithms / Randomness / Resorts & spas / Source code / Summer