Cuckoo search algorithm: overview, modifications, and applications

Abstract

The Cuckoo Search Algorithm (CSA), introduced by Xin-She Yang and Suash Deb in 2009, is a nature-inspired metaheuristic optimization technique modeled on the brood parasitism behavior of certain cuckoo bird species. Utilizing a Levy flight mechanism, CSA effectively balances global exploration and local exploitation, making it a versatile tool for addressing non-linear, multi-modal, and high-dimensional optimization problems. This paper presents a comprehensive exploration of CSA, detailing its biological foundation, mathematical framework, and algorithmic processes. Key modifications, including hybrid approaches, adaptive mechanisms, and domain-specific enhancements, are reviewed to illustrate how CSA has been refined to tackle increasingly complex optimization challenges. Applications spanning engineering, machine learning, energy systems, robotics, and telecommunications highlight CSA’s versatility and efficiency in solving real-world problems. Despite its strengths, challenges such as parameter sensitivity and computational demands in large-scale scenarios persist. To address these, avenues for future research are proposed, including the integration of CSA with emerging technologies like quantum computing and advanced machine learning techniques. This study underscores CSA’s role as a cornerstone of modern metaheuristic optimization, offering a robust framework for solving diverse and challenging problems.