Chaos Optimization Algorithms: A Survey

Chaos Optimization Algorithms (COAs) have emerged as a potent global optimization technique, exhibiting remarkable capabilities in tackling complex optimization challenges. In recent years, a plethora of COAs and their variants have been developed and employed to address a wide array of practical problems in engineering and science. Despite the proliferation of these algorithms, existing reviews often suffer from being outdated or lacking in robust classification criteria. This deficiency impedes the accurate assessment of the latest advancements and obstructs researchers’ ability to swiftly comprehend the current state of research in chaos optimization. To bridge this gap, this paper offers a review by classifying chaos optimization into five distinct categories: chaos map-based optimization algorithms, chaos metaheuristic optimization algorithms, chaos game optimization algorithms, hybrid optimization algorithms, and others. Each category is meticulously analyzed, with a thorough discussion of the inherent strengths and weaknesses of the respective algorithms. This analysis not only clarifies the unique features of each algorithm but also enhances understanding by contrasting their various applications. The review extends to the practical deployment of chaos algorithms across specific problems, illustrating their versatility and effectiveness. Conclusively, this paper delineates potential future research evolution of COAs, providing a clear and structured guide to forthcoming explorations in this dynamic field. This survey aims to empower researchers within the optimization community with a deeper and more comprehensive understanding of the landscape of COAs, paving the way for innovative research and applications.