MATLAB Implementation of Cuckoo Search Algorithm with Levy Flight

This article introduces the fundamental concepts of the Cuckoo Search Algorithm to facilitate better understanding of its principles and advantages. The Cuckoo Search Algorithm is an optimization technique based on flight behavior, simulating how cuckoos search for food and mating partners. The algorithm's key characteristics include global optimization capability and rapid convergence speed. In the algorithm implementation, the Cuckoo Search Algorithm incorporates Levy flight - a random flight behavior with long-tailed distribution that enhances the algorithm's exploration capacity and improves search efficiency. In MATLAB code, this is typically implemented using power-law distributions to generate step sizes, often through mantegna's algorithm for Levy flight simulations. The core algorithm structure involves nesting cycles where host nests represent potential solutions, and new solutions are generated via Levy flights around the best current solutions. The application domains of Cuckoo Search Algorithm are extensive, covering optimization problems, data mining, pattern recognition, and more. For instance, in engineering design, the algorithm can optimize design parameters to achieve optimal performance and results. In financial sectors, it can be applied to stock price prediction and portfolio optimization. Additionally, the algorithm finds applications in power load forecasting, image processing, and signal processing. The MATLAB implementation typically involves defining objective functions, setting population size parameters, and implementing the replacement strategy where poorer solutions are abandoned based on discovery probabilities. Overall, the Cuckoo Search Algorithm is a highly useful optimization technique whose unique flight behavior and global optimization capabilities have led to widespread application and development across numerous fields. This article aims to help readers better understand the Cuckoo Search Algorithm and leverage its advantages in practical applications, with the MATLAB code providing hands-on implementation experience including parameter tuning and convergence monitoring features.