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

Gorilla troops optimizer (GTO) is a newly developed meta-heuristic algorithm, which inspired by the collective lifestyle and social intelligence of gorillas. Similar to other metaheuristics, convergence accuracy stability GTO will deteriorate when optimization problems be solved become more complex flexible. To overcome these defects achieve better performance, this paper proposes an improved gorilla (IGTO). First, Circle chaotic mapping introduced initialize positions gorillas, facilitates population diversity establishes good foundation for global search. Then, in order avoid getting trapped local optimum, lens opposition-based learning mechanism adopted expand search ranges. Besides, novel search-based namely adaptive β-hill climbing, amalgamated with increase final solution precision. Attributed three improvements, exploration exploitation capabilities basic are greatly enhanced. The performance proposed algorithm comprehensively evaluated analyzed on 19 classical benchmark functions. numerical statistical results demonstrate that IGTO can provide quality, optimum avoidance, robustness compared five wellknown algorithms. Moreover, applicability further proved through resolving four engineering design training multilayer perceptron. experimental suggest exhibits remarkable competitive promising prospects real-world tasks.