A Novel Hybrid Optimization Technique Using Greylag Goose Optimization and Crayfish Optimization
Rekha Gaitond, G. S. Biradar · Preprints.org · 2024
Optimization techniques inspired by natural behaviors have demonstrated effectiveness in solving complex and high-dimensional problems. This work presents a novel hybrid optimization algorithm that combines Greylag Goose Optimization (GGO) and Crayfish Optimization (CO), designed to balance global and local search mechanisms. GGO, based on the social and migratory behaviors of geese, provides robust exploration, while CO offers refined local search capabilities inspired by crayfish foraging patterns. This hybrid approach leverages adaptive switching criteria, dynamically transitioning between GGO and CO to prevent premature convergence. Experimental results on standard benchmark functions demonstrate the superiority of this hybrid technique in terms of convergence speed, accuracy, and robustness when compared to individual algorithms and other optimization methods. This work contributes a new nature-inspired optimization model with potential applications in engineering, complex resource allocation and security in cloud environments.