Novel Hybrid Crayfish Optimization Algorithm and Self-Adaptive Differential Evolution for Solving Complex Optimization Problems

• Published in: Symmetry (Basel) Vol. 16; no. 7; p. 927
• Main Authors: Fakhouri, Hussam N., Ishtaiwi, Abdelraouf, Makhadmeh, Sharif Naser, Al-Betar, Mohammed Azmi, Alkhalaileh, Mohannad
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Adaptive algorithms; Algorithms; Analysis; Artificial intelligence; Computer engineering; Design; Design engineering; Design optimization; Design parameters; Efficiency; Evolutionary algorithms; Evolutionary computation; Exploitation; Hybridization; Linear algebra; Linear programming; Machine learning; Mathematical optimization; Mutation; Optimization algorithms; Optimization techniques; Performance evaluation; Statistical analysis; Symmetry; Morocco; Germany
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym16070927

This study presents the Hybrid COASaDE Optimizer, a novel combination of the Crayfish Optimization Algorithm (COA) and Self-adaptive Differential Evolution (SaDE), designed to address complex optimization challenges and solve engineering design problems. The hybrid approach leverages COA’s efficient exploration mechanisms, inspired by crayfish behaviour, with the symmetry of SaDE’s adaptive exploitation capabilities, characterized by its dynamic parameter adjustment. The balance between these two phases represents a symmetrical relationship wherein both components contribute equally and complementary to the algorithm’s overall performance. This symmetry in design enables the Hybrid COASaDE to maintain consistent and robust performance across a diverse range of optimization problems. Experimental evaluations were conducted using CEC2022 and CEC2017 benchmark functions, demonstrating COASaDE’s superior performance compared to state-of-the-art optimization algorithms. The results and statistical analyses confirm the robustness and efficiency of the Hybrid COASaDE in finding optimal solutions. Furthermore, the applicability of the Hybrid COASaDE was validated through several engineering design problems, where COASaDE outperformed other optimizers in achieving the optimal solution.