An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization

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.

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“…The performance of the GWO algorithm can be improved by the design of IGWO algorithm with the inclusion of adaptive β ‐hill climbing (A β HC). It is a recently presented local search-based technique, that is, basically, a modified version of β ‐hill climbing ( βHC ) [ 20 ]. The study has established that A β HC gives optimum performance than several other famous local search techniques.…”

An Intelligent Feedforward Controller Utilizing a Modified Gorilla Troops Optimization for Nonlinear Systems

“…Nonetheless, some male gorillas prefer to reside with the original troop and follow the silverback. If the silverback perishes, these males may engage in a violent struggle for group dominance and mate with adult females [19].…”

“…Similar to other intelligent algorithms, GTO is comprised of three major parts: initialization, global exploration, and local exploitation. Each of these is explained in more detail below [19].…”

“…GTO was inspired by the way gorillas live as a group and how intelligent they are in social situations. Research so far shows that GTO does a great job on benchmark function optimization [19]. In addition, in order to increase the exploration ability of the original GTO algorithm by adopting more optimal solutions to the optimization process, an enhanced version, termed the modified gorilla troops optimization (MGTO) algorithm, is proposed in this study to optimize the controller's parameters.…”

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“…Compared with traditional algorithms, they are superior in handling complex optimization problems. However, the most basic meta-heuristic algorithm still has the disadvantages of easily falling into local optimum, slow convergence speed, and poor convergence accuracy when solving some complex optimization problems [20]. Consequently, the fundamental meta-heuristic algorithm has been improved by numerous scholars and applied to path planning problems.…”