Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/30854
Title: | Applying ant colony optimization to dynamic binary-encoded problems | Authors: | Mavrovouniotis, Michalis Yang, Shengxiang |
Major Field of Science: | Natural Sciences | Field Category: | Computer and Information Sciences | Keywords: | Algorithms;Ant colony optimization;Artificial intelligence;Combinatorial optimization;Optimization;Traveling salesman problem | Issue Date: | 8-Apr-2015 | Source: | 18th European Conference on the Applications of Evolutionary Computation, EvoApplications 2015, Copenhagen, 8 - 10 April 2015 | Volume: | 9028 | Conference: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Abstract: | Ant colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when stagnation behaviour is addressed. Usually, permutation-encoded DOPs, e.g., dynamic travelling salesman problems, are addressed using ACO algorithms whereas binary-encoded DOPs, e.g., dynamic knapsack problems, are tackled by evolutionary algorithms (EAs). This is because of the initial developments of the introduced to address binary-encoded DOPs and compared with existing EAs. The experimental results show that ACO with an appropriate pheromone evaporation rate outperforms EAs in most dynamic test cases. | URI: | https://hdl.handle.net/20.500.14279/30854 | ISBN: | 9783319165486 | ISSN: | 03029743 | DOI: | 10.1007/978-3-319-16549-3_68 | Rights: | © Springer International Publishing | Type: | Conference Papers | Affiliation : | De Montfort University |
Appears in Collections: | Άρθρα/Articles |
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