Metaheuristics: A Review of Algorithms

In science and engineering, many optimization tasks are difficult to solve, and the core concern these days is to apply metaheuristic (MH) algorithms to solve them. Metaheuristics have gained significant attention in recent years, with nature serving as the fundamental inspiration where self-organization property led to collective intelligence emerging from the behavior of a swarm of birds or colony of insects or more and more natural behavior. These swarms or colonies, even with extremely low individual competence, have the ability to accomplish many complicated activities that can be considered necessary for their existence. Accordingly, many MH algorithms have been developed based on natural phenomena. In this article, an analysis review of more than one hundred metaheuristics have been made. Further, the main contributions of this article are to give some vital insights about metaheuristics, presenting and proposing the general mathematical framework of MH algorithms and dividing it into a number of tasks with possible progress for each task. While there are still many open issues in this field, it is worth noting that there have been significant advancements in recent years. As a result, new algorithms are continuously being proposed to address these challenges.