A novel quasi-reflected Harris hawks optimization algorithm for global optimization problems
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METHODOLOGIES AND APPLICATION
A novel quasi-reflected Harris hawks optimization algorithm for global optimization problems Qian Fan1
•
Zhenjian Chen1 • Zhanghua Xia1
Ó Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Harris hawks optimization (HHO) is a recently developed meta-heuristic optimization algorithm based on hunting behavior of Harris hawks. Similar to other meta-heuristic algorithms, HHO tends to be trapped in low diversity, local optima and unbalanced exploitation ability. In order to improve the performance of HHO, a novel quasi-reflected Harris hawks algorithm (QRHHO) is proposed, which combines HHO algorithm and quasi-reflection-based learning mechanism (QRBL) together. The improvement includes two parts: the QRBL mechanism is introduced firstly to increase the population diversity in the initial stage, and then, QRBL is added in each population position update to improve the convergence rate. The proposed method will also be helpful to control the balance between exploration and exploitation. The performance of QRHHO has been tested on twenty-three benchmark functions of various types and dimensions. Through comparison with the basic HHO, HHO combined with opposition-based learning mechanism and HHO combined with quasi-oppositionbased learning mechanism, the results demonstrate that QRHHO can effectively improve the convergence speed and solution accuracy of the basic HHO and two variants of HHO. At the same time, QRHHO is also better than other swarmbased intelligent algorithms. Keywords Harris hawks optimization Quasi-reflection-based learning Opposition-based learning Benchmark functions Swarm-based intelligent algorithms
1 Introduction In recent years, meta-heuristic algorithms have attracted extensive attention in various fields. Compared with the traditional optimization algorithms, the meta-heuristic algorithms are simple in principle and easy to implement. Also, the algorithms do not need gradient information and have the advantages of bypassing local optimal and thus have been widely used to solve the optimization problems in various disciplines or engineering applications. The meta-heuristic algorithms mainly include optimization algorithms based on evolution, physics, human and swarm (Mirjalili and Lewis 2016), such as genetic algorithm (GA) (Holland 1992), differential evolution (DE)
Communicated by V. Loia. & Qian Fan [email protected] 1
College of Civil Engineering, Fuzhou University, Fuzhou 350116, China
(Storn and Price 1997), simulated annealing (SA) (Kirkpatrick et al. 1983), particle swarm optimization algorithm (PSO) (Kennedy and Eberhart 2002), artificial bee colony algorithm (ABC)(Karaboga and Basturk 2007), Krill Herd (KH) (Gandomi and Alavi 2012), gravitational search algorithm (GSA) (Rashedi et al. 2009), fruit fly optimization algorithm (FOA) (Pan 2012), ant lion optimizer (ALO)(Mirjalili 2015a), moth-flame optimization (MFO) (Mirjalili 2015b), grey wolf optimizer (GWO) (Mirjalili et al. 2014
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