a Review of Metaheuristic Algorithms in Optimization



With continuously increasing complexity of optimization problems and poor performance of conventional analytical based methods, more powerful tools are required to cope these problems. Difficulties such as necessity of differentiable and continuous model as well as possibility of converging to local minimum, computational time of these methods increase exponentially as well. Metaheuristic algorithms have introduced to overcome such challenges. These methods don’t require differentiation information, can discover global optimal and run away from local optima using their operators with linear or polynomial increase in their computational time. However, because of diversity and different publication resource of these methods, researchers don’t know their characteristic and search mechanism well. This paper aims to introduce some of the most important of these algorithms (40 different algorithms), to describe main characteristic of these algorithms such as solution space search method, main operators and their inspiration sources. Moreover, some of unique characteristic of these algorithms such as local and global search capability, memory consideration and parameters tuning methods are discussed.


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