Improved elephant herding optimization algorithm based on sine cosine search
Haoyu Luo · 2023
Inspired by the renewal process of elephant clans, elephant herding optimization (EHO) is proposed and successfully applied to many optimization problems. However, EHO is prone to fall into local optimum during the optimization process, resulting in poor global search ability. To overcome this deficiency, an improved elephant herding optimization (SCEHO) is proposed, which integrate good point set strategy, improved sine cosine search strategy, nonuniform gauss mutation strategy and greedy strategy. First, good point set is introduced to initialize the population. Secondly, sine cosine algorithm is improved and incorporated into EHO to update the position of individuals. Then, the separation operator is improved from two aspects, on the one hand, non-uniform Gauss mutation is introduced to separate individuals. On the other hand, the number of separated individuals is increased, and half of the individuals with poor fitness are selected. Finally, the greedy strategy is introduced into SCEHO to update population according to the fitness. Our SCEHO is tested on 10 benchmark functions from CEC 2019 and the results imply the superiority of SCEHO to other algorithms. SCEHO is also applied to solve WSN coverage optimization problem. The results show that coverage model based on SCEHO has higher coverage and more uniform node distribution.