Grey Parrot Optimization Algorithm

With the widespread application of computational intelligence and swarm intelligence optimization algorithms, designing optimization algorithms that combine global search capabilities with local precision search capabilities has become a research hotspot. This paper proposes an optimization algorithm based on the behavioral characteristics of grey parrots, called the Grey Parrot Optimization (GPO). This algorithm draws on the grey parrot's behavioral characteristics such as imitation learning, social interaction, foraging exploration, and cognitive memory. By constructing an individual hierarchical imitation mechanism, a dynamic social network, adaptive exploration step size, and a local perturbation strategy, it achieves efficient solutions to continuous function optimization problems. This paper provides a detailed derivation of the algorithm's mathematical model and demonstrates the specific implementation steps through pseudocode and formulas. Experimental results show that the proposed algorithm outperforms traditional particle swarm optimization and ant colony optimization algorithms in terms of global convergence, local search accuracy, and resistance to premature convergence.