Incorporation of Improved Differential Evolution into Hunger Games Search Algorithm

Abstract

Hunger games search (HGS) is a recently proposed meta-heuristic algorithm based on hunger-driven activities and animal behavior choices, and it has been proven to possess global exploration ability and can solve both constrained and unconstrained problems effectively. Differential evolution (DE) algorithm is a heuristic random search algorithm based on the evolution differences among different individuals, and it can exploit local regions within a neighborhood of an individual. To fully utilize characteristics of both algorithms, this paper for the first time proposes a hybrid algorithm based on HGS and DE. Furthermore, DE is also enhanced by a new ranking-based mechanism to generate more promising solutions. Experimental results on IEEE CEC2017 benchmark functions indicate the effectiveness of the hybrid algorithm, namely DEHGS, in comparison with some other state-of-the-art algorithms.