Chaos Game Optimization: A comprehensive study of its variants, applications, and future directions

Abstract

Chaos Game Optimization Algorithm (CGO) is a novel advancement in metaheuristic optimization inspired by chaos theory. It addresses complex optimization problems in dynamical systems, exhibiting unique behaviours such as fractals and self-organized patterns. CGO’s design exemplifies adaptability and robustness, making it a significant tool for tackling intricate optimization scenarios. This study presents a comprehensive and updated overview of CGO, exploring the various variants and adaptations that have been published in numerous research studies since its introduction in 2020, with 4% in book chapters, 7% in international conference proceedings, and 89% in prestigious international journals. CGO variants covered in this paper include 4% binary, 22% for multi-objective and modification and 52% for hybridization variants. Moreover, the applications of CGO, demonstrate its efficacy and flexibility across different domains with 32% in energy, 28% in engineering, 11% in IoT and machine learning, 6% in truss structures, 4% in big data, 2% in medical imaging, in security, in electronic, and in microarray technology. Furthermore, we discuss the future directions of CGO, hypothesizing its potential advancements and broader implications in optimization theory and practice. The primary objectives of this survey paper are to provide a comprehensive overview of CGO, highlighting its innovative approach, discussing its variants and their usage in different sectors, and the burgeoning interest it has sparked in metaheuristic algorithms. As a result, this manuscript is expected to offer valuable insights for engineers, professionals across different sectors, and academic researchers.