Improvement and Application of Ant Colony Algorithm Based on Dynamic Candidate List

DOI

10.2991/icmse-15.2015.189How to use a DOI?

Keywords

Ant colony algorithm (ACA), partial optimum, dynamic candidate list, entropy.

Abstract

The basic ant colony algorithm converges slowly, is prone to plunge into partial optimum. This paper proposes an improved ant colony algorithm with two highlights. First, dynamic candidate list (DCL) strategy is introduced. In the route construction, candidate routes, whose fitness value surpass some criterion, will be put into DCL. Dynamic candidate strategy is adopted to rapid convergence speed. Second, by using the population’s entropy to evaluate the evolution state, the algorithm dynamically adjusts heuristic parameter based on entropy, adapting to different searching stages. The simulation results verify the validity of the improved algorithm.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hong Zhang
AU  - Zhixin Liu
PY  - 2015/12
DA  - 2015/12
TI  - Improvement and Application of Ant Colony Algorithm Based on Dynamic Candidate List
BT  - Proceedings of the 2015 6th International Conference on Manufacturing Science and Engineering
PB  - Atlantis Press
SP  - 1040
EP  - 1043
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmse-15.2015.189
DO  - 10.2991/icmse-15.2015.189
ID  - Zhang2015/12
ER  -