Year: 2024
Type: article
Abstract: In response to the deficiencies of the Crayfish Optimization Algorithm (COA), such as slow convergence speed and susceptibility to local optima when solving complex optimization problems, an improved Crayfish Optimization Algorithm (ICOA) with a hybrid strategy is proposed. This improvement incorporates five strategies: an enhanced position update function for followers from the Sparrow Algorithm, a refined spiral position update function from the Whale Optimization Algorithm, modifications to the criteria and methods for assessing food size, the golden ratio coefficient, and an information accumulation function. Optimization tests conducted on 12 benchmark functions demonstrate that the improved algorithm shows enhancements in both convergence speed and optimization performance. Finally, experiments applying the improved optimization algorithm to engineering application problems further validate the superiority of the enhanced Crayfish Optimization Algorithm.
Author: Tong Ding
Institution: Guizhou University of Finance and Economics
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W6893174744
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