A Comprehensive Review on Scatter Search: Techniques, Applications, and Challenges

Recent years have witnessed the use of metaheuristic algorithms to solve the optimization problems that usually require extensive computations and time. Among others, scatter search is the widely used evolutionary metaheuristic algorithm. It uses the information of global optima, which is stored in a different and unique set of solutions. In this paper, an updated review of scatter search (SS) is given. SS has been successfully applied in a variety of research problems, namely, data mining, bioinformatics, and engineering design. This paper presents the modified and hybrid versions of SS with their applications. The control strategies are discussed to show their impact on the performance of SS. various issues and future directions related to SS are also discussed. It inspires innovative researchers to use this algorithm for their research domains.

Automated summary

Scatter search is a commonly used evolutionary metaheuristic algorithm that has been successfully applied in various research fields, such as data mining, bioinformatics, and engineering design. This paper presents an updated review of scatter search, including discussions on control strategies, issues, and future directions related to its application. It aims to inspire innovative researchers to utilize this algorithm in their own research domains.