Forest Gecko Optimization Algorithm

The forest gecko, a highly adaptable small reptile, demonstrates remarkable abilities in both precise local searching and random global exploration within complex forest environments, providing bio-inspired insights for optimization algorithms. This paper proposes a novel swarm intelligence optimization algorithm—the Forest Gecko Optimization Algorithm (FGOA). This algorithm achieves an efficient combination of global search and local exploitation by simulating the gecko's foraging, jumping, crawling, and information-sharing behaviors. The paper elaborates on the algorithm modeling, mathematical formula derivation, behavioral mechanism analysis, and algorithm flow, proposing innovative mechanisms such as polymorphic search, adaptive step size, dynamic neighbor cooperation, chaotic perturbation, and self-learning weights to improve the algorithm's search efficiency, convergence accuracy, and ability to escape local optima.