Journal of Bionic Engineering ›› 2022, Vol. 19 ›› Issue (6): 1850-1885.doi: 10.1007/s42235-022-00223-y
Differential Evolution-Boosted Sine Cosine Golden Eagle Optimizer with Lévy Flight
Gang Hu1,2; Liuxin Chen1; Xupeng Wang3; Guo Wei4
- 1 Department of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, People’s Republic of China 2 School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, People’s Republic of China 3 School of Art and Design, Xi’an University of Technology, Xi’an 710054, China 4 Department of Mathematics and Computer Science, University of North Carolina at Pembroke, Pembroke, NC 28372, USA
Differential Evolution-Boosted Sine Cosine Golden Eagle Optimizer with Lévy Flight
Gang Hu1,2; Liuxin Chen1; Xupeng Wang3; Guo Wei4#br#
- 1 Department of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, People’s Republic of China 2 School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, People’s Republic of China 3 School of Art and Design, Xi’an University of Technology, Xi’an 710054, China 4 Department of Mathematics and Computer Science, University of North Carolina at Pembroke, Pembroke, NC 28372, USA
摘要： Golden eagle optimizer (GEO) is a recently introduced nature-inspired metaheuristic algorithm, which simulates the spiral hunting behavior of golden eagles in nature. Regrettably, the GEO suffers from the challenges of low diversity, slow iteration speed, and stagnation in local optimization when dealing with complicated optimization problems. To ameliorate these deficiencies, an improved hybrid GEO called IGEO, combined with Lévy flight, sine cosine algorithm and differential evolution (DE) strategy, is developed in this paper. The Lévy flight strategy is introduced into the initial stage to increase the diversity of the golden eagle population and make the initial population more abundant; meanwhile, the sine–cosine function can enhance the exploration ability of GEO and decrease the possibility of GEO falling into the local optima. Furthermore, the DE strategy is used in the exploration and exploitation stage to improve accuracy and convergence speed of GEO. Finally, the superiority of the presented IGEO are comprehensively verified by comparing GEO and several state-of-the-art algorithms using (1) the CEC 2017 and CEC 2019 benchmark functions and (2) 5 real-world engineering problems respectively. The comparison results demonstrate that the proposed IGEO is a powerful and attractive alternative for solving engineering optimization problems.