An Improved Hunter-prey Optimization Algorithm and Its Application*

Abstract

Hunter-prey optimization (HPO) algorithm is a new optimization algorithm proposed to simulate the behavior of leopards, lions and other predators in hunting deer and antelope. In order to solve the problems such as insufficient global optimization capability of HPO, easy to fall into local optimization, and low optimization accuracy, an improved Hunter-prey optimization (IHPO) algorithm is proposed. Firstly, Tent chaotic map is used to generate the initial population and increase the diversity of individuals, Secondly, in order to balance the ability of global search in the early stage and local search in the late stage, the enhanced sine cosine algorithm (ESCA) is integrated to adaptively select the population location update mode according to the conversion probability, Finally, Cauchy mutation strategy is adopted in the later stage of iteration to disturb the population position and enhance the ability of the algorithm to jump out of precocity. The simulation results of benchmark functions show that IHPO algorithm has better convergence accuracy and convergence speed. The effectiveness of the improved algorithm is further verified by the simulation experiment of pipe routing examples