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New metaheuristic algorithms
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Abstract: (1320 Views) |
| Abstract - The complexity of mathematical models, exponential growth of the solution time for many methods, lack of access to gradient information and optimal local convergence are some of the problems that optimal classical algorithms face in solving complex problems. In order to eliminate these drawbacks, metaheuristic algorithms are widely used to solve complex and multivariate problems. Choosing the best and most suitable algorithm is difficult due to their high diversity. In previous studies, some of these methods have been summarized, but due to overpublicize of these methods in recent years, there is no specific article to describe and compare all of these methods. In this paper, the most important metaheuristic optimization algorithms are introduced from 2012 till now. In separate sections for each algorithm, the history, source of inspiration, objective function and number of its setting parameters are stated. These algorithms are then categorized and compared using several theories. Due to the type of application of each algorithm in engineering problems, it is not possible to introduce a single algorithm as the best methodology, but the Gray Wolf Optimization (GWO) algorithm is one of the algorithms with a high number of citations in recent years. |
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| Keywords: Optimization, Metaheuristic algorithms, Evolution, Particle swarm optimization |
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Full-Text [PDF 957 kb]
(2721 Downloads)
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Type of Study: Scientific-extension |
Subject:
Special
Received: 2021/03/14 | Accepted: 2020/12/20 | Published: 2020/12/20
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