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Conference Papers Year : 2018

Elite Opposition-Based Selfish Herd Optimizer

Abstract

Selfish herd optimizer (SHO) is a new metaheuristic optimization algorithm for solving global optimization problems. In this paper, an elite opposition-based Selfish herd optimizer (EOSHO) has been applied to functions. Elite opposition-based learning is a commonly used strategy to improve the performance of metaheuristic algorithms. Elite opposition-based learning enhances the search space of the algorithm and the exploration of the algorithm. An elite opposition-based Selfish herd optimizer is validated by 7 benchmark functions. The results show that the proposed algorithm is able to obtain the more precise solution, and it also has a high degree of stability.
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hal-02197808 , version 1 (30-07-2019)

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Shengqi Jiang, Yongquan Zhou, Dengyun Wang, Sen Zhang. Elite Opposition-Based Selfish Herd Optimizer. 10th International Conference on Intelligent Information Processing (IIP), Oct 2018, Nanning, China. pp.89-98, ⟨10.1007/978-3-030-00828-4_10⟩. ⟨hal-02197808⟩
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