Chaotic guided local search algorithm for solving global optimization and engineering problems

Chaos optimization algorithm (COA) is an interesting alternative in a global optimization problem. Due to the non-repetition and ergodicity of chaos, it can explore the global search space at higher speeds than stochastic searches that depend on probabilities. To adjust the solution obtained by COA, guided local search algorithm (GLS) is integrated with COA to form a hybrid algorithm. GLS is a metaheuristic optimization algorithm that combines elements of local search with strategic guidance to efficiently explore the solution space. This study proposes a chaotic guided local search algorithm to search for global solutions. The proposed algorithm, namely COA-GLS, contributes to optimization problems by providing a balance between quick convergence and good solution quality. Its combination of local refinement, strategic guidance, diversification strategies, and adaptability makes it a powerful metaheuristic capable of efficiently navigating complex solution spaces and finding high-quality solutions in a relatively short amount of time. Simulation results show that the present algorithms significantly outperform the existing methods in terms of convergence speed, numerical stability, and a better optimal solution than other algorithms.