OCSA: An Optimized Cuckoo Search Algorithm with Guided Exploration and Refined Local Search

This paper introduces an optimized version of the Cuckoo Search Algorithm (CSA), termed the Optimized CSA (OCSA), designed to address key limitations of traditional population-based optimization methods, including slow convergence, limited precision, and high memory usage. OCSA improves both exploration and exploitation through two main contributions. First, it employs a multi-directional guided displacement strategy around the current best solution, enhancing the algorithm’s ability to explore promising regions more effectively. Second, it strengthens local search by having each cuckoo generate multiple candidate solutions and selecting the best among them, which improves solution accuracy and accelerates convergence. The performance of OCSA is evaluated using benchmark functions, engineering design problems, and a real-world application in economic dispatch. Comparative evaluations against established algorithms, including Cooperative Coevolutionary CS (CCCS), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), as well as mathematical solvers such as Interior Point OPTimizer (IPOPT) and Linear and Objective Quadratic (based on an infeasible primal-dual interior-point) Optimizer (LOQO), demonstrate that OCSA achieves superior convergence speed, solution quality, and scalability. These results establish OCSA as an efficient optimization approach for complex problems.

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