Research Area: Metaheuristic Computing
In recent years, research on stochastic optimization algorithms has received more and more attention from researchers, especially bio-inspired optimization algorithms. Selfish herd optimizer (SHO) is a novel bio-inspired optimization algorithm. It has features that are easy to understand and implement. However, its global search ability is insufficient and precision needs to be further improved. Therefore, we add levy-flight distribution strategy to improve its global search ability and precision. Our main contribution is to use selfish herd optimizer with levy-flight distribution strategy (LFSHO) to solve function and engineering example optimization problem. From experiment results, we can see that LFSHO has more advantages than other algorithms, according to precision, convergence speed and standard variance. It can conclude that LFSHO is a new method for solving function optimization problem and engineering example optimization problem.
Keywords:
stochastic optimization algorithm
selfish herd optimizer
levy-flight
distribution strategy
precision
convergence speed
standard variance
Author(s) Name: Ruxin Zhao, Yongli Wang, Chang Liu, Peng Hu, Yanchao Li, Hao Li, Chi Yuan
Journal name: Physica A: Statistical Mechanics and its Applications
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.physa.2019.122687
Volume Information: Volume 538
