Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies

By Hao Chen, Ali Asghar Heidari, Huiling Chen, Mingjing Wang, Zhifang Pan, Amir H. Gandomi. Future generations computer systems, 2020

300 citations recorded by the source corpus.

Abstract The first powerful variant of the Harris hawks optimization (HHO) is proposed in this work. HHO is a recently developed swarm-based stochastic algorithm that has previously shown excellent performance. In fact, the original HHO has features that can still be improved as it may experience convergence problems or may easily become trapped in local optima. To overcome these shortcomings of the original HHO, the first powerful variant of HHO integrates chaos strategy, topological multi-population strategy, and differential evolution (DE) strategy. For this, chaos mechanism is first introduced into the original algorithm to improve the exploitation propensities of HHO. The multi-population strategy with three mechanisms is embedded to augment the global search ability of the method. Finally, the DE mechanism is introduced into the HHO to enhance the quality of the solutions. Based on these well-regarded evolutionary mechanisms, we propose an enhanced DE-driven multi-population HHO (CMDHHO) algorithm. In this work, the proposed CMDHHO is compared with a range of other methods, including four original meta-heuristic algorithms, conventional HHO, twelve advanced algorithms based on IEEE CEC2017 benchmark functions, and IEEE CEC2011 real-world problems. Furthermore, the Friedman test and the non-parametric statistical Wilcoxon sign rank test are used to verify the significance of the results. The results of the experiments show that the three embedded mechanisms can effectively enhance the exploratory and exploitative traits of HHO. The time required for HHO to converge was substantially shortened. We suggest using the proposed CMDHHO as an effective tool to solve complex optimization problems.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/e851978798b161286f430ce6de13a634ed26a089

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Huiling Chen
2. Ali Asghar Heidari
3. Majdi M. Mafarja
4. Hamza Turabieh
5. Zhou Wei
6. Pengjun Wang
7. Xuehua Zhao
8. Tansel Dökeroğlu

In Computational Theory and Mathematics

1. Huiling Chen
2. Ali Asghar Heidari
3. Hamza Turabieh
4. Zhou Wei
5. Pengjun Wang
6. Xuehua Zhao
7. Majdi M. Mafarja

Citations in the corpus

• Metaphor-free dynamic spherical evolution for parameter estimation of photovoltaic modules (2021, background): “…et al., 2020c,e; Li et al., 2017a; Liu et al., 2015), parameter optimization (Heidari et al., 2019a; Shen et al., 2016; Wang et al., 2017b; Wang and Chen, 2020), prediction problems in educational field (Wei et al., 2020; Zhu et al., 2020; Lin et al., 2019; Tu et al., 2019; Wei et al., 2017),…”