Rajwar Kanchan, Deep Kusum, Das Swagatam
Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667 India.
Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal 700108 India.
Artif Intell Rev. 2023 Apr 9:1-71. doi: 10.1007/s10462-023-10470-y.
As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called 'novel' if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on 'novel ideas', so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.
随着世界迈向工业化，要在合理时间内解决优化问题变得更具挑战性。迄今为止，已开发出500多种新的元启发式算法（MA），其中超过350种是在过去十年出现的。近年来，相关文献大量增加，需要进行全面综述。在本研究中，追踪了约540种MA，并提供了统计信息。由于近年来MA的大量涌现，不同名称的算法之间存在大量相似性的问题已普遍存在。这就引出了一个关键问题：如果一种优化技术的搜索特性被修改或几乎等同于现有方法，它还能被称为“新颖的”吗？许多最近的MA据说基于“新颖的想法”，因此对它们进行了讨论。此外，本研究根据控制参数的数量对MA进行分类，这是该领域一种新的分类法。MA作为强大的优化工具已在各个领域广泛应用，并展示了它们的一些实际应用。已识别出一些局限性和开放挑战，这可能为MA未来的发展指明新方向。尽管研究人员在过去十年的多篇研究论文、综述文章和专著中报告了许多出色的成果，但仍有许多未探索的领域有待发现。本研究将帮助新手了解元启发式算法的一些主要领域及其实际应用。我们预计这一资源对我们的研究群体也将有用。
