Seeker Evolutionary Algorithm (SEA): a Novel Algorithm for Continuous Optimization

Nowadays, in majority of academic contexts, it has been tried to consider the highest possible level of similarities to the real world. Hence, most of the problems have complicated structures. Traditional methods for solving almost all of the mathematical and optimization problems are inefficient. As a result, meta-heuristic algorithms have been employed increasingly during recent years. In this study, a new algorithm, namely Seeker Evolutionary Algorithm (SEA), is introduced for solving continuous mathematical problems, which is based on a group seeking logic. In this logic, the seekingregion and the seekers located inside are divided into several sections and they seek in that special area. In order to assess the performance of this algorithm, from the available samples in papers, the most visited algorithms have been employed. The obtained results show the advantage of the proposed SEA incomparison to these algorithms. At the end, a mathematical problem is designed, which is unlike the structure of meta-heuristic algorithms. All the prominent algorithms are applied to solve this problem, and none of them is able to solve.