An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

Abstract

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.

Synergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances

“…Metaheuristic algorithms [244,245] represent iterative methods using a "basic" heuristic procedure to smartly combine higher-level strategies for search space, avoid local optima and find an approximation for the glob Metaheuristics are mimicking various natural or sociological phenomena (e human collective behavior, their physiological processes and plant beha mathematical procedures, physical, astrophysical and chemical phenomen This mimicking is the reason why these algorithms are often designa phor-based. The number of metaheuristic methods is huge and is still incre Basic heuristics themselves include a number of approaches.…”

Maximizing solar radiations of PV panels using artificial gorilla troops reinforced by experimental investigations

“…Although GA was the first MH method introduced by Holland 47 , GA is still the most used optimization method as mentioned in a survey in Ref. 50 . A good review on GA is presented in Ref.…”

Installation of Smog-Free Towers Using Novel Real Coded Genetic Algorithm

“…RCGA have been widely used in areas such artificial intelligence [21], agriculture sector [22], pattern recognition [23], traveling salesman problem [24] and many more. Although they do not ensure the exact optimum value, but they try to give the best possible optimal feasible solution [25], [26]. RCGAs, are very well suited to continuous optimization problems with a large search space.…”