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Research on convergence of ACO subset algorithms

Wenyu Chen (School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China)
Wangyang Bian (School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China)
Ru Zeng (School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China)

Abstract

Purpose

The purpose of this paper is to show that the theoretical proofs of convergence in solution of ant colony optimization (ACO) algorithms have significant values of theory and application.

Design/methodology/approach

This paper adapts the basic ACO algorithm framework and proves two important ACO subclass algorithms which are ACObs,τmin  and ACObs,τmin (t).

Findings

This paper indicates that when the minimums of pheromone trial decay to 0 with the speed of logarithms, it is ensured that algorithms can, at least, get a certain optimal solution. Even if the randomicity and deflection of random algorithms are disturbed infinitesimally, algorithms can obtain optimal solution.

Originality/value

This paper focuses on the analysis and proof of the convergence theory of ACO subset algorithm to explore internal mechanism of ACO algorithm.

Keywords

Citation

Chen, W., Bian, W. and Zeng, R. (2013), "Research on convergence of ACO subset algorithms", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 32 No. 2, pp. 649-660. https://doi.org/10.1108/03321641311297089

Publisher

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Emerald Group Publishing Limited

Copyright © 2013, Emerald Group Publishing Limited

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