A comprehensive survey on the sine–cosine optimization algorithm

Abstract Metaheuristic algorithms based on intelligent rules have been successfully developed and applied to solve many optimization areas over the past few decades. The sine–cosine algorithm (SCA) imitates behaviour of transcendental functions while sine cosine are presented explore exploit search space. SCA starts by random population executes iterative evolution processes update standard evolutionary algorithm’s destination or best location. used linear transition balance exploration exploitation searches searching for optimal solutions. Since Mirjalili proposed it in 2016, has attracted researchers’ attention deal with several problems fields due its strengths solving tasks that include simple concept, easiness implementation, rapid convergence. This paper aims provide researchers a relatively comprehensive extensive overview Sine–Cosine literature inspire further research. It examines available publications, including improvements, binary, chaotic, hybridizations, multi-objective variants, different applications. Some formulations regarding single-objective problems, binary-objective more classifications types discussed. An bibliography is also included.