Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5823
Title: Ant colony optimization with immigrants schemes in dynamic environments
Authors: Mavrovouniotis, M
Yang, S
Keywords: Ant colony optimization;Immigrants schemes;Dynamic optimization
Issue Date: 2010
Publisher: Springer-Verlag
Citation: 11th International Conference on Parallel Problems Solving from Nature (PPSN XI), Part II, Kraków, Poland, 6239: 371 - 380, 2010-09-11 - 2010-09-15
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, elitismbased, 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.
Description: This is the post-print version of this article. The official published version can be accessed from the link below - Copyright @ 2010 Springer-Verlag
URI: http://bura.brunel.ac.uk/handle/2438/5823
DOI: http://dx.doi.org/10.1007/978-3-642-15871-1_38
ISBN: 3-642-15870-6
978-3-642-15870-4
ISSN: 0302-9743
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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