A Novel Hybrid Hunger Games Search Algorithm With Differential Evolution for Improving the Behaviors of Non-Cooperative Animals

作     者：Li, Shaolang Li, Xiaobo Chen, Hui Zhao, Yuxin Dong, Junwei 

作者机构：Zhejiang Normal Univ Coll Math & Comp Sci Jinhua 321004 Zhejiang Peoples R China Lishui Univ Coll Engn Lishui 323000 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2021年第9卷

页      面：164188-164205页

核心收录：

基　　金：National Natural Science Foundation of China Science and Technology Planning Project of Lishui City [2019RC05] 

主　　题：Statistics Sociology Optimization Licenses Clustering algorithms Search problems Particle swarm optimization Hunger games search differential evolution algorithm chaotic local search evolutionary population dynamics optimization 

摘      要：Inspired by the behaviors of animals in the state of starvation, hunger games search algorithm (HGS) is proposed. HGS has shown competitive performance among other meta-heuristic (MH) algorithms. However, HGS tends to stagnate in local optimal for some complex optimization problems and remains premature convergence. Therefore, to solve these problems and enhance the diversity of the population, a modified HGS based on the operators of the differential evolution algorithm (DE), chaotic local search (CLS) strategies, and evolutionary population dynamics technique (EPD) is proposed (named DECEHGS). The proposed DECEHGS algorithm consists of two stages: in the first stage, based on the animals behaviors, we use different evolutionary methods to update animals positions;in the second stage, the CLS strategy and EPD technique are combined to prevent premature convergence and stagnation in a local optimum. The proposed algorithm was evaluated using IEEE CEC2014 and IEEE CEC2017 mathematical functions and four engineering problems. The experimental results demonstrate that DECEHGS has competitive performance in global optimization tasks and engineering problems compared with state-of-the-art algorithms.