A Chaotic Boost: The Chaotic Crayfish Optimization Algorithm for Superior Solution Quality
Binanda Maiti, Saptadeep Biswas, Uttam Kumar Bera, Madhujit Deb, Heming Jia, Kashif Saleem, Hazem Migdady, Aseel Smerat, Laith Abualigah · Optimal Control Applications and Methods · 2025
ABSTRACT This study introduces the Chaotic Crayfish Optimization Algorithm (CCOA), an advanced variant of the Crayfish Optimization Algorithm (COA) that integrates chaotic maps to enhance its performance in solving complex global optimization and engineering problems. The COA, inspired by the foraging behaviour of crayfish, has demonstrated effectiveness but is challenged by issues such as an imbalance between exploration and exploitation, a tendency to get trapped in local optima, and slower convergence rates in high‐dimensional landscapes. By incorporating chaotic dynamics, the CCOA addresses these limitations, improving the algorithm's ability to navigate diverse regions of the solution space and refine promising solutions. The CCOA employs ten distinct chaotic maps to dynamically adapt its search strategies dynamically, optimizing the exploration‐exploitation balance. The algorithm is rigorously evaluated against established benchmark test functions from recognized competitions, including CEC 2014, CEC 2017, CEC 2020, and CEC 2022, to assess its effectiveness in finding optimal solutions. A comprehensive comparative analysis is conducted against various well‐known optimization algorithms, including Particle Swarm Optimization, Differential Evolution, and the traditional COA, among others. Statistical significance is established through average ranking and Wilcoxon Rank Sum Test evaluations. Additionally, the CCOA is applied to six real‐world engineering design problems, such as the Welded Beam Design Problem and the Cantilever Beam Design Problem, demonstrating its practical applicability and effectiveness. The results indicate that the CCOA significantly enhances convergence speed and solution quality while effectively escaping local optima, establishing it as a robust tool for addressing a wide range of optimization challenges. This work contributes to the expanding field of metaheuristic optimization, showcasing the potential of chaotic maps to improve algorithmic performance and applicability in complex problem domains.