混沌博弈优化:对其变体、应用和未来方向的全面研究

IF 13.3 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Raja Oueslati , Ghaith Manita , Amit Chhabra , Ouajdi Korbaa
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引用次数: 0

摘要

混沌博弈优化算法(CGO)是受混沌理论启发而在元启发式优化方面取得的新进展。它能解决动态系统中的复杂优化问题,表现出分形和自组织模式等独特行为。CGO 的设计体现了适应性和鲁棒性,使其成为解决复杂优化问题的重要工具。本研究全面介绍了 CGO 的最新概况,探讨了自 2020 年 CGO 问世以来在大量研究中发表的各种变体和适应性,其中 4% 发表在书籍章节中,7% 发表在国际会议论文集中,89% 发表在著名国际期刊上。本文涉及的 CGO 变体包括 4% 的二元变体、22% 的多目标变体和修正变体,以及 52% 的混合变体。此外,CGO 在不同领域的应用证明了它的有效性和灵活性,其中能源领域占 32%,工程领域占 28%,物联网和机器学习领域占 11%,桁架结构领域占 6%,大数据领域占 4%,医学成像、安全、电子和微阵列技术领域占 2%。此外,我们还讨论了 CGO 的未来发展方向,假设其在优化理论和实践中的潜在进步和更广泛的影响。本调查报告的主要目的是全面概述 CGO,突出其创新方法,讨论其变体及其在不同领域的应用,以及它在元搜索算法中引发的蓬勃兴趣。因此,本手稿有望为工程师、不同行业的专业人士和学术研究人员提供有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Chaos Game Optimization: A comprehensive study of its variants, applications, and future directions

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.

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来源期刊
Computer Science Review
Computer Science Review Computer Science-General Computer Science
CiteScore
32.70
自引率
0.00%
发文量
26
审稿时长
51 days
期刊介绍: Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.
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