Cuckoo Search (CS) Algorithm: Implementation and Optimization Principles

Cuckoo Search (CS), also known as Cuckoo Optimization Algorithm, is an emerging metaheuristic algorithm proposed by Professor Xin-She Yang of Cambridge University and S. Deb in 2009. The algorithm efficiently solves optimization problems by simulating brood parasitism behavior of cuckoo species, combined with Levy flight search mechanisms. Research demonstrates CS outperforms other swarm intelligence algorithms in convergence speed and solution accuracy, making it suitable for complex engineering optimization tasks.

Cuckoo Search Algorithm MATLAB Implementation with Code Descriptions

Application Background: Cuckoo Search (CS) algorithm, also known as Cuckoo Optimization, is an emerging metaheuristic algorithm proposed by Professor Xin-She Yang from Cambridge University and S. Deb in 2009. This algorithm effectively solves optimization problems by simulating the brood parasitic behavior of certain cuckoo species, combined with Levy flight search mechanisms. Research demonstrates that CS outperforms many other swarm optimization algorithms. Key Technical Aspects: This MATLAB implementation simulates cuckoo nesting behavior through three idealized rules with position updates using Levy flights. The code includes parameter configuration for population size, discovery probability, and step size control, providing a practical framework for solving engineering optimization and machine learning problems.