Abstract
A multi-colony ant colony optimization (ACO) algorithm consists of several colonies of ants. Each colony uses a separate pheromone table in an attempt to maximize the search area explored. Over the years, multi-colony ACO algorithms have been successfully applied on different optimization problems with stationary environments. In this paper, we investigate their performance in dynamic environments. Two types of algorithms are proposed: homogeneous and heterogeneous approaches, where colonies share the same properties and colonies have their own (different) properties, respectively. Experimental results on the dynamic travelling salesman problem show that multi-colony ACO algorithms have promising performance in dynamic environments when compared with single colony ACO algorithms.
Original language | English |
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Title of host publication | 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), Proceedings |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 9-16 |
Number of pages | 8 |
ISBN (Print) | 9781479945160 |
DOIs | |
Publication status | Published - 12 Jan 2015 |
Event | 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2014 - Orlando, United States Duration: 9 Dec 2014 → 12 Dec 2014 |
Conference
Conference | 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2014 |
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Country/Territory | United States |
City | Orlando |
Period | 9/12/14 → 12/12/14 |
ASJC Scopus subject areas
- Artificial Intelligence
- Computational Theory and Mathematics
- Computer Science Applications