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PREACO: A fast ant colony optimization for codebook generation

delete2013-06-01
delete27
PRE
AI
C
Chun‐Wei Tsai
S
Shih-Pang Tseng
C
Chu‐Sing Yang
M
Ming‐Chao Chiang *
DOI:10.1016/j.asoc.2013.01.017delete
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Abstract

Abstract

En 中文
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. (C) 2013 Elsevier B. V. All rights reserved.
Keywords:
Ant colony optimization
Codebook generation problem
Pattern reduction
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Journal

Applied Soft Computing cover
Applied Soft Computing
IF:
6.6
Papers:
1.4W
Citations:
4.8W

Organization

N
National Cheng Kung University
Scholars:
2.6W
Papers: 2.3W
Citations: 1.7W
C
chia nan university of pharmacy & science
Scholars:
825
Papers: 981
Citations: 0
N
national sun yat sen university
Scholars:
7.6K
Papers: 7.7K
Citations: 3
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