A hybrid grasshopper optimization algorithm with bat algorithm for global optimization
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A hybrid grasshopper optimization algorithm with bat algorithm for global optimization Shenghan Yue 1 & Hongbo Zhang 1 Received: 8 April 2020 / Revised: 5 July 2020 / Accepted: 15 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
This paper introduces a hybrid grasshopper optimization algorithm with bat algorithm (BGOA) for global optimization. In the BGOA, the Levy flight with variable coefficient is employed to enhance the exploration capability of the GOA. Then, the local search operation of bat algorithm (BA) is combined to balance the exploration and exploitation. Additionally, the random strategy is introduced and applied to high quality population to improve the exploitation capability in the searching process. The performance of BGOA is evaluated on 23 benchmark test functions, and compares with genetic algorithm (GA), bat algorithm (BA), moth-flame optimization algorithm (MFO), dragonfly algorithm (DA) and basic GOA. The results establish that the BGOA is able to provide better outcomes than the other algorithms. Keywords Grasshopper optimization algorithm . Bat algorithm . Levy flight . Random strategy . Global optimization
1 Introduction Optimization exists in many fields, such as job-shop scheduling [15], path planning [22], power control [28] and image segmentation [19]. The computation complexity of traditional techniques is high for non-convexity and high–dimensionality optimization problems [2]. In the past decade, various swarm intelligence (SI) algorithms had been proposed to solve the optimization problems. Some of the most typical algorithms are genetic algorithm (GA) [7], particle swarm optimization (PSO) [4] and artificial bee colony (ABC) [8]. The recent algorithms include bat algorithm (BA) [25], lightning search algorithm (LSA) [21] and whale optimization algorithm [13].
* Shenghan Yue [email protected]
1
School of Mechatronic Engineering, Changchun University of Technology, Changchun 130012, People’s Republic of China
Multimedia Tools and Applications
The grasshopper optimization algorithm (GOA) is one of the latest SI algorithms that simulates the behavior of grasshoppers. In recent years, the GOA has drawn much attention of researchers and applied to many fields due to its efficiency and effectiveness [18]. In 2017, Wu J et al. applied adaptive grasshopper optimization algorithm (AGOA) to the trajectory optimization of Solar-powered unmanned aerial vehicle (SUAV) [24]. In 2018, Zhang X et al. used GOA-based method to analyze vibration signals [30]. In 2019, Hazra S et al. introduced a GOA-based approach on economical operation [6]. Similar to other optimization algorithms, the GOA also has drawbacks with regard to premature convergence and low searching precision [5]. To address these drawbacks, various versions of GOA have been proposed by researchers. Arora S and Anand P combined chaotic maps into the GOA, and evaluated the effectiveness of each map in improving the GOA. The experimental results proved that the chaotic maps signifi
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