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<p>Optimization is an important branch of engineering, artificial intelligence, and data science that aims to find the best solution among possible solutions. Given the increasing complexity of optimization problems, using traditional methods is often inefficient and challenging. In contrast, metaheuristic algorithms, inspired by natural, biological, and social phenomena, can strike a good balance between global and local search and provide optimal answers in a reasonable time. One of the algorithms invented in 2021 is the Chameleon Swarm Algorithm (CSA), which has achieved remarkable performance in solving complex optimization problems. The CSA literature lacks a comprehensive, structured, and analytical review. So far, no source has reviewed research on improved versions, hybrid algorithms, parameter modification methods, chaotic, fuzzy, OBL, and quantum models, as well as extensive practical applications of CSA in a single framework. This paper presents a comprehensive and integrated picture of the scientific status of CSA and future research opportunities, systematically reviewing 130 papers. The statistical analyses show that the largest number of papers are published by Elsevier (31%) and Springer (25%). In this paper, CSA papers are classified into four main categories: Hybrid methods (23%) represent the combination of CSA with other metaheuristic algorithms. Improved methods (35%) help improve CSA performance by using strategies such as chaotic maps, opposition-based learning, and quantum computing. Variants (6%) represent binary and multi-objective formulations of the CSA. Applications (36%) address the use of the CSA to solve optimization problems in engineering, medicine, and management.</p>

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A survey of chameleon swarm algorithm and its variants: recent developments, structural review, meta-analysis, and theoretical perspectives

  • 期刊论文

  • 发表日期：

    2026年6月2日

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  • Sang-Woong Lee,
  • Saqib Ali,
  • Amir Masoud Rahmani,
  • Ramin Abbaszadi,
  • Farhad Soleimanian Gharehchopogh,
  • Parisa Khoshvaght,
  • Mehdi Hosseinzadeh

摘要

Optimization is an important branch of engineering, artificial intelligence, and data science that aims to find the best solution among possible solutions. Given the increasing complexity of optimization problems, using traditional methods is often inefficient and challenging. In contrast, metaheuristic algorithms, inspired by natural, biological, and social phenomena, can strike a good balance between global and local search and provide optimal answers in a reasonable time. One of the algorithms invented in 2021 is the Chameleon Swarm Algorithm (CSA), which has achieved remarkable performance in solving complex optimization problems. The CSA literature lacks a comprehensive, structured, and analytical review. So far, no source has reviewed research on improved versions, hybrid algorithms, parameter modification methods, chaotic, fuzzy, OBL, and quantum models, as well as extensive practical applications of CSA in a single framework. This paper presents a comprehensive and integrated picture of the scientific status of CSA and future research opportunities, systematically reviewing 130 papers. The statistical analyses show that the largest number of papers are published by Elsevier (31%) and Springer (25%). In this paper, CSA papers are classified into four main categories: Hybrid methods (23%) represent the combination of CSA with other metaheuristic algorithms. Improved methods (35%) help improve CSA performance by using strategies such as chaotic maps, opposition-based learning, and quantum computing. Variants (6%) represent binary and multi-objective formulations of the CSA. Applications (36%) address the use of the CSA to solve optimization problems in engineering, medicine, and management.