A hybrid differential evolution based on gaining‑sharing knowledge algorithm and harris hawks optimization

Xuxu Zhong,Xiao Zhang,Peng Cheng,Meijun Duan,Seyedali Mirjalili

doi:10.1371/journal.pone.0250951

Xuxu Zhong, Xiao Zhang + Show 3 more

Open Access

https://doi.org/10.1371/journal.pone.0250951

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Journal: PloS one	Publication Date: Apr 30, 2021
Citations: 6	License type: CC BY 4.0

Affiliation: Sichuan University, Xihua University

Abstract

Differential evolution (DE) is favored by scholars for its simplicity and efficiency, but its ability to balance exploration and exploitation needs to be enhanced. In this paper, a hybrid differential evolution with gaining-sharing knowledge algorithm (GSK) and harris hawks optimization (HHO) is proposed, abbreviated as DEGH. Its main contribution lies are as follows. First, a hybrid mutation operator is constructed in DEGH, in which the two-phase strategy of GSK, the classical mutation operator “rand/1” of DE and the soft besiege rule of HHO are used and improved, forming a double-insurance mechanism for the balance between exploration and exploitation. Second, a novel crossover probability self-adaption strategy is proposed to strengthen the internal relation among mutation, crossover and selection of DE. On this basis, the crossover probability and scaling factor jointly affect the evolution of each individual, thus making the proposed algorithm can better adapt to various optimization problems. In addition, DEGH is compared with eight state-of-the-art DE algorithms on 32 benchmark functions. Experimental results show that the proposed DEGH algorithm is significantly superior to the compared algorithms.

Highlights

Whether in the field of science or engineering, problem optimization is a hot topic
DEGH is compared with eight enhanced differential evolution (DE) algorithms including IMMSADE [19], collective information-powered DE (CIPDE) [20], EBDE [21], EDE [21], EJADE [30], LSHADE-SPACMA [35], DE and particle swarm optimization (DEPSO) [37] and adaptive teaching–learningbased optimization with DE (ATLDE) [39] at D = 30,100
As one of the control parameters of DE, the influence of population size NP on the performance of DEGH is studied on the 32 benchmark functions at D = 30

Summary

Introduction

Many researchers are keen to use meta-heuristic algorithms to solve optimization problems, leading to the emergence of various meta-heuristic algorithms, such as Evolution strategies (ES) [1], genetic algorithm (GA) [2], differential evolution [3], particle swarm optimization (PSO) [4], artificial bee colony (ABC) [5], gravitational search algorithm (GSA) [6], teaching–learningbased optimization (TLBO) [7], moth-flame optimization (MFO) [8], whale optimization algorithm (WOA) [9], harris hawks optimization (HHO) [10] and gaining-sharing knowledge algorithm (GSK) [11]. Differential evolution (DE) has become one of the most commonly used meta-heuristic algorithms for solving optimization problems [12]. A Hybrid differential evolution based on gaining-sharing knowledge algorithm and Harris hawks optimization [14], optics [15], energy [16] and neural network [17]. Improvements studies to DE can be divided into two broad categories: 1) Changes of DE compositions, which enhance the performance of the original DE by improving the mutation, crossover, selection operation and adjusting control parameters; 2) hybrid DE with other meta-heuristic algorithms to improve performance by combing their respective advantages

Results

Discussion

Conclusion