Abstract Inspired by the intricate group dynamics of wild gorilla populations, the Artificial Gorilla Troops Optimizer (GTO) represents a novel approach in swarm intelligence. Despite its effectiveness in performing global exploration, GTO is prone to early convergence and can easily become stuck in local optima, especially when addressing optimization problems with intricate constraints and rugged search spaces. To overcome these limitations, this paper introduces the Multi-Strategy Integrated Gorilla Troops Optimizer (MSIGTO), which integrates Latin Hypercube Sampling (LHS), Lévy Flight (LF), and the Cauchy Inverse Cumulative Distribution Operator (CICDO). The diversity of the initial population is enhanced through LHS, and the exploration and convergence characteristics of the algorithm are further improved by LF and CICDO. To assess its effectiveness, a comparative analysis is conducted between MSIGTO and various advanced optimization techniques: Parrot Optimizer (PO), Fata Morgana Algorithm (FATA), Weighted Mean of Vectors (INFO), Pelican Optimization Algorithm (POA), Hunger Games Search (HGS), Linear-SHADE (L-SHADE), African Vultures Optimization Algorithm (AVOA), and the original GTO. The evaluation is conducted using the IEEE CEC2022 benchmark set and four practical engineering case studies. The experimental analysis demonstrates that MSIGTO exhibits notable advantages regarding global exploration ability, convergence efficiency, and solution robustness.
Teng et al. (Mon,) studied this question.