Open Access
ARTICLE
An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization
Yaning Xiao, Xue Sun*, Yanling Guo, Sanping Li, Yapeng Zhang, Yangwei Wang
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, China
* Corresponding Author: Xue Sun. Email:
(This article belongs to this Special Issue: Swarm Intelligence and Applications in Combinatorial Optimization)
Computer Modeling in Engineering & Sciences 2022, 131(2), 815-850. https://doi.org/10.32604/cmes.2022.019198
Received 08 September 2021; Accepted 28 October 2021; Issue published 14 March 2022
Abstract
Gorilla troops optimizer (GTO) is a newly developed meta-heuristic algorithm, which is inspired by the collective
lifestyle and social intelligence of gorillas. Similar to other metaheuristics, the convergence accuracy and stability of
GTO will deteriorate when the optimization problems to be solved become more complex and flexible. To overcome
these defects and achieve better performance, this paper proposes an improved gorilla troops optimizer (IGTO).
First, Circle chaotic mapping is introduced to initialize the positions of gorillas, which facilitates the population
diversity and establishes a good foundation for global search. Then, in order to avoid getting trapped in the local
optimum, the lens opposition-based learning mechanism is adopted to expand the search ranges. Besides, a novel
local search-based algorithm, namely adaptive β-hill climbing, is amalgamated with GTO to increase the final
solution precision. Attributed to three improvements, the exploration and exploitation capabilities of the basic
GTO are greatly enhanced. The performance of the proposed algorithm is comprehensively evaluated and analyzed
on 19 classical benchmark functions. The numerical and statistical results demonstrate that IGTO can provide
better solution quality, local optimum avoidance, and robustness compared with the basic GTO and five other wellknown algorithms. Moreover, the applicability of IGTO is further proved through resolving four engineering design
problems and training multilayer perceptron. The experimental results suggest that IGTO exhibits remarkable
competitive performance and promising prospects in real-world tasks.
Keywords
Cite This Article
Xiao, Y., Sun, X., Guo, Y., Li, S., Zhang, Y. et al. (2022). An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization.
CMES-Computer Modeling in Engineering & Sciences, 131(2), 815–850.
Citations