Abstract
Spherical evolution (SE) is a relatively innovative algorithm. It transforms the original hypercube search into a spherical search. By this novel search style, SE expanded the search range. Sine cosine algorithm (SCA) creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions, which demonstrates that this algorithm can avoid local optima. In this article, we introduce SCA to enhance the convergence ability of SE. The experiment results on CEC2017 benchmark functions indicate the effectiveness of this hybridization, suggesting that the algorithm proposed is able to explore different regions of a search space, avoid local optima, converge towards the global optimum, and exploit promising regions of a search space during optimization effectively.
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