The hybridized Harris hawk optimization and slime mould algorithm

Abstract

Both the Harris hawk optimization (HHO) algorithm proposed in 2016 and the slime moud algorithm proposed recently had complicated disciplines for individuals to update their positions. And both of them were proved to be capable of finding the best solutions for either benchmark functions or real engineering problems. In this paper, we further hybridized the SM and HHO algorithms and allowed the individuals in swarms to take more ways to update their positions. Simulation experiments were carried out and the better performance in either accuracy or convergence rate verified the capability of the hybridization.