UM
A mathematical analysis of improved EigenAnt algorithm
Gou G.2; Zhao Y.2; Li Q.2; Xu Q.1
2019-03-04
Source PublicationJournal of Experimental and Theoretical Artificial Intelligence
ISSN13623079 0952813X
Volume31Issue:2Pages:291-309
AbstractAs a variant of Ant Colony Optimization, the EigenAnt algorithm finds the shortest path between a source node and a destination node based on negative feedback in the form of selective pheromone removal that occurs only on the path which is actually chosen for each trip. EigenAnt algorithm also could change quickly to reflect to the dynamic variety of initial pheromone concentrations and path length etc. However, in general, the solution of EigenAnt algorithm is not always convergent. In this paper, we propose an improved EigenAnt (iEigenAnt) algorithm in terms of both negative and positive feedback; that is, selective pheromone updates are decided by smart ants or stupid ones, which depends whether the amount of the pheromone at the selected path increases or not. The system modelled by our algorithm has a unique equilibrium as the shortest path. Besides, using mathematical analysis, we demonstrate that the equilibrium is global asymptotically stable, i.e., stable and convergent. Finally, we also implement the iEigenAnt algorithm under four different cases and apply it on travelling salesman problem problem, the simulation result shows that our iEigenAnt algorithm is faster convergent and more effective compared to the original EigenAnt algorithm, and some combinatorial optimisation problems can be effectively solved based on our iEigenAnt algorithm.
KeywordAnt colony optimisation convergence analysis EigenAnt Algorithm stability analysis swarm intelligence
DOI10.1080/0952813X.2018.1544203
URLView the original
Language英語
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Universidade de Macau
2.East China Normal University
3.Shanghai Zhongyi Industrial Control Tech. Company, Ltd.
Recommended Citation
GB/T 7714
Gou G.,Zhao Y.,Li Q.,et al. A mathematical analysis of improved EigenAnt algorithm[J]. Journal of Experimental and Theoretical Artificial Intelligence,2019,31(2):291-309.
APA Gou G.,Zhao Y.,Li Q.,&Xu Q..(2019).A mathematical analysis of improved EigenAnt algorithm.Journal of Experimental and Theoretical Artificial Intelligence,31(2),291-309.
MLA Gou G.,et al."A mathematical analysis of improved EigenAnt algorithm".Journal of Experimental and Theoretical Artificial Intelligence 31.2(2019):291-309.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Gou G.]'s Articles
[Zhao Y.]'s Articles
[Li Q.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Gou G.]'s Articles
[Zhao Y.]'s Articles
[Li Q.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Gou G.]'s Articles
[Zhao Y.]'s Articles
[Li Q.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.