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

What is it about?

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).

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Photo by Paula Robinson on Unsplash

Photo by Paula Robinson on Unsplash

Why is it important?

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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