PREACO: A fast ant colony optimization for codebook generation

This paper presents an effective and efficient method for speeding up ant colony optimization (ACO) in solving the codebook generation problem. The proposed method is inspired by the fact that many computations during the convergence process of ant-based algorithms are essentially redundant and thus can be eliminated to boost their convergence speed, especially for large and complex problems. To evaluate the performance of the proposed method, we compare it with several state-of-the-art metaheuristic algorithms. Our simulation results indicate that the proposed method can significantly reduce the computation time of ACO-based algorithms evaluated in this paper while at the same time providing results that match or outperform those ACO by itself can provide.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► A fast ant colony optimization for codebook generation called PREACO is proposed. ► Simulations show that PREACO can reduce up to 99.51% the running time of ACS. ► Simulations show that PREACO provides an efficient method for complex problems. ► Ideally, PREACO makes the time complexity of ACS independent of iterations.