Enhanced Competitive Swarm Optimization (ECSO) algorithm for solving optimization problems
DOI:
https://doi.org/10.54153/sjpas.2026.v8i2.1348Keywords:
Competitive, Swarm, Optimization, exploitation, and exploration.Abstract
This article introduces the Enhanced Competitive Swarm Optimization (ECSO) algorithm, which was developed to improve the balance of exploration and exploitation in difficult optimization situations. The proposed ECSO uses adaptive control parameters and an improved leader-follower mechanism to efficiently direct the search agents to the global optimum while avoiding premature convergence. To assess its performance, ECSO was run through a series of standard benchmark functions from the CEC2021 test suite and compared to various well-known metaheuristic algorithms. The comparative research revealed that ECSO consistently produced the lowest average fitness values with the smallest standard deviation, indicating higher precision, stability, and robustness. These findings demonstrate that ECSO strikes a good balance between global exploration and local exploitation, making it a dependable and powerful optimization strategy for solving high-dimensional and nonlinear optimization problems.
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