Adaptive Elite Cuckoo Search Algorithm with Step-Size Control and Elitism Crossover for Global Optimization

Abstract

Cuckoo Search Algorithm (CSA) has emerged as a powerful bio-inspired metaheuristic, yet its reliance on random-walk search often leads to slow convergence and susceptibility to local optima. This study introduces an Adaptive Elite Cuckoo Search Algorithm (AECSA), designed to enhance both convergence speed and solution precision. The proposed modifications integrate two key mechanisms: (i) an adaptive step-size strategy that dynamically adjusts the Lévy flight scale to balance global exploration and local exploitation, and (ii) an elitist crossover scheme that enables information sharing among the top-performing solutions to accelerate progress toward the global optimum. The AECSA was validated across six classical benchmark functions under extensive simulations. Results demonstrate that AECSA consistently outperforms the standard CSA, achieving up to 85% faster convergence and significantly improved solution accuracy, with statistical validation via a two-tailed t-test. These findings highlight the robustness and efficiency of AECSA, making it a promising approach for tackling complex, high-dimensional optimization problems in engineering and computational intelligence.