Abstract
Recently, a new population-based optimization algorithm called spherical evolution (SE), which is inspired by the traditional hyper-cube search style, has been proposed. It has significant ability of exploration and can avoid local optimum. On the other hand, salp swarm algorithm (SSA) has great advantage in local optimization of current random solutions. In this study, we are devoted to incorporating SSA into SE for solving optimization problems. In this hybrid algorithm, SE contributes to enhance the exploration during its iteration, and SSA is supposed to accelerate the convergence to optimal solutions. The experiment results on IEEE CEC 2017 benchmark functions indicate the effectiveness of this hybridization and show that spherical search style and salp swarm search mechanism are complementary. This study gives not only more insights into both original algorithms, but also a novel construction method of merging different algorithms.
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