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An Improved EigenAnt Algorithm That Achieves a Balance between Intensification and Diversification
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Version 1
: Received: 7 December 2018 / Approved: 11 December 2018 / Online: 11 December 2018 (10:18:40 CET)
Version 2 : Received: 16 June 2019 / Approved: 17 June 2019 / Online: 17 June 2019 (04:06:33 CEST)
Version 2 : Received: 16 June 2019 / Approved: 17 June 2019 / Online: 17 June 2019 (04:06:33 CEST)
How to cite: Mahrueyan, M.; Bhaya, A. An Improved EigenAnt Algorithm That Achieves a Balance between Intensification and Diversification. Preprints 2018, 2018120123 Mahrueyan, M.; Bhaya, A. An Improved EigenAnt Algorithm That Achieves a Balance between Intensification and Diversification. Preprints 2018, 2018120123
Abstract
The EigenAnt algorithm has been proposed to find the shortest path between two nodes with a proof of convergence. Two novel features are noticeable in EigenAnt compared to the conventional ant colony optimization algorithms. Pheromone evaporation in the EigenAnt algorithm is done locally for the selected path and it does not use heuristic information in its selection phase. On the other hand, a simple version of ant colony optimization that also does not use heuristic information has been used for analyzing convergence, considering parameter impact analysis, in the problem of finding the shortest path for 1-node binary chain problems. In this paper, we propose to improve the EigenAnt algorithm by adding additional parameters in such a way as to be able to take advantage of the analysis developed for the simple ant colony optimization algorithm. We demonstrate through our analysis that since the Improved EigenAnt algorithm has one parameter more than ant colony optimization algorithms, tuning of convergence speed is decoupled from tuning of convergence features in the algorithm. Thus, IEigenAnt, which can be interpreted as having two uncorrelated intensification and diversification components, makes it possible to achieve a balance between intensification and diversification. In order to illustrate the advantages of this decoupling, we present experimental results for the routing network problem.
Keywords
EigenAnt; parameter impact analysis; intensification and diversification balance; routing networks
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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