Research Area: Metaheuristic Computing
The selfish herds optimizer (SHO) is a recently developed swarm optimization algorithm for solving global optimization problems. SHO mimics the widely observed selfish herd behaviors of avoiding predation risks. In SHO, a set of unique evolutionary operators inspired by the prey-predator relationship are used in dealing with optimization problems. Although SHO can provide a good performance when finding an optimal solution, this algorithm has some oversights that cause problems in the exploitation ability of SHO. Additionally, SHO still has some shortcomings that influence the performance of exploration and avoiding stagnation in local optima. In this paper, some refinements and modifications are proposed for these oversights and shortcomings. In order to validate whether the refinements and modifications are appropriate for improving the performance of SHO, two suites of benchmark functions are employed to compare the proposed method with the original SHO and other well-known and recently developed algorithms, such as Standard Particle Swarm Optimization, Artificial Bee Colony algorithm, Differential Evolution, and Crow Search Algorithm, etc. Finally, the efficiency of the proposed method is justified using the nonparametric Wilcoxon rank-sum test. The experimental results show that the proposed modifications are appropriate for improving the performance of the original SHO. In addition, the proposed method can also give competitive results in finding global optima when compared with other algorithms.
Keywords:
Particle Swarm Optimization
Artificial Bee Colony algorithm
Differential Evolution
Crow Search
selfish herds optimizer
swarm optimization algorithm
prey-predator
Author(s) Name: Adiljan Yimit, Koji Iigura, Yoshihiro Hagihara
Journal name: Expert Systems with Applications
Conferrence name:
Publisher name: Elsevier
DOI: https://doi.org/10.1016/j.eswa.2019.112838
Volume Information: Volume 139
