Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30873
Title: Ant colony optimization with immigrants schemes in dynamic environments
Authors: Mavrovouniotis, Michalis 
Yang, Shengxiang 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Ant Colony Optimization;Dynamic Optimization;Immigrants Schemes
Issue Date: 12-Nov-2010
Source: 11th International Conference on Parallel Problem Solving from Nature, PPSN 2010, 11 - 15 September 2010
Volume: 6239 LNCS
Issue: PART 2
Conference: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 
Abstract: In recent years, there has been a growing interest in addressing dynamic optimization problems (DOPs) using evolutionary algorithms (EAs). Several approaches have been developed for EAs to increase the diversity of the population and enhance the performance of the algorithm for DOPs. Among these approaches, immigrants schemes have been found beneficial for EAs for DOPs. In this paper, random, elitism-based, and hybrid immigrants schemes are applied to ant colony optimization (ACO) for the dynamic travelling salesman problem (DTSP). The experimental results show that random immigrants are beneficial for ACO in fast changing environments, whereas elitism-based immigrants are beneficial for ACO in slowly changing environments. The ACO algorithm with hybrid immigrants scheme combines the merits of the random and elitism-based immigrants schemes. Moreover, the results show that the proposed algorithms outperform compared approaches in almost all dynamic test cases and that immigrant schemes efficiently improve the performance of ACO algorithms in DTSP. © 2010 Springer-Verlag.
URI: https://hdl.handle.net/20.500.14279/30873
ISBN: 3642158706
ISSN: 03029743
DOI: 10.1007/978-3-642-15871-1_38
Rights: © Springer-Verlag
Type: Conference Papers
Affiliation : University of Leicester 
Brunel University London 
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