Enhanced crayfish optimization algorithm with differential evolution’s mutation and crossover strategies for global optimization and engineering applications

• Published in: The Artificial intelligence review 2025-01, Vol.58 (3), p.69, Article 69
• Main Authors: Maiti, Binanda, Biswas, Saptadeep, Ezugwu, Absalom El-Shamir, Bera, Uttam Kumar, Alzahrani, Ahmed Ibrahim, Alblehai, Fahad, Abualigah, Laith
• Format: Article
• Language: English
• Subjects: Design engineering; Design optimization; Evolutionary algorithms; Evolutionary computation; Global optimization; Mutation; Optimization algorithms; Particle swarm optimization; Performance evaluation; Search algorithms; Solution space
• ISSN: 1573-7462; 0269-2821; 1573-7462
• DOI: 10.1007/s10462-024-11069-7

Optimization algorithms play a crucial role in solving complex challenges across various fields, including engineering, finance, and data science. This study introduces a novel hybrid optimization algorithm, the Hybrid Crayfish Optimization Algorithm with Differential Evolution (HCOADE), which addresses the limitations of premature convergence and inadequate exploitation in the traditional Crayfish Optimization Algorithm (COA). By integrating COA with Differential Evolution (DE) strategies, HCOADE leverages DE’s mutation and crossover mechanisms to enhance global optimization performance. The COA, inspired by the foraging and social behaviors of crayfish, provides a flexible framework for exploring the solution space, while DE’s robust strategies effectively exploit this space. To evaluate HCOADE’s performance, extensive experiments are conducted using 34 benchmark functions from CEC 2014 and CEC 2017, as well as six engineering design problems. The results are compared with ten leading optimization algorithms, including classical COA, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Moth-flame Optimization (MFO), Salp Swarm Algorithm (SSA), Reptile Search Algorithm (RSA), Sine Cosine Algorithm (SCA), Constriction Coefficient-Based Particle Swarm Optimization Gravitational Search Algorithm (CPSOGSA), and Biogeography-based Optimization (BBO). The average rankings and results from the Wilcoxon Rank Sum Test provide a comprehensive comparison of HCOADE’s performance, clearly demonstrating its superiority. Furthermore, HCOADE’s performance is assessed on the CEC 2020 and CEC 2022 test suites, further confirming its effectiveness. A comparative analysis against notable winners from the CEC competitions, including LSHADEcnEpSin, LSHADESPACMA, and CMA-ES, using the CEC-2017 test suite, revealed superior results for HCOADE. This study underscores the advantages of integrating DE strategies with COA and offers valuable insights for addressing complex global optimization problems.