Selfish herd optimizer with levy-flight distribution strategy for global optimization problem

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. (C) 2019 Elsevier B.V. All rights reserved.