NDLI: Unsupervised fuzzy clustering-based genetic algorithms to Traveling Salesman Problem

Content Provider	IEEE Xplore Digital Library
Author	Jebari, K. El moujahid, A. Bouroumi, A. Ettouhami, A.
Copyright Year	2012
Description	Author affiliation: Laboratoire Conception et Systèmes (Microélectronique et Informatique), Faculty of Sciences, Mohammed V-Agdal University, UM5A, Rabat, Morocco (Jebari, K.; El moujahid, A.; Bouroumi, A.; Ettouhami, A.)
Abstract	In this paper a new genetic algorithm based on an unsupervised fuzzy clustering is proposed for Traveling Salesman Problem (TSP). The proposed algorithm involves on three phases. In the first phase, the cities are divided into several sub-tours by the clustering algorithm. In the second phase, each partition of cities is considered as a smaller scale TSP problem and this smaller size TSP problem is solved by a genetic algorithm which gets an optimal sub-tour of the cities of this partition. In the third phase, a new technique for connecting all these sub-tours into an appropriate tour of whole cities. As well as, this appropriate tour is improved by a genetic algorithm for cluster centers and a heuristic method. The computer simulations on some standard test problems show good performance for the proposed algorithm.
Starting Page	1013
Ending Page	1015
File Size	681701
Page Count	3
File Format	PDF
ISBN	9781467315180
e-ISBN	9781467315203
DOI	10.1109/ICMCS.2012.6320145
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-10
Publisher Place	Morocco
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cities and towns Genetic algorithms Clustering algorithms Sociology Statistics Traveling salesman problems Partitioning algorithms Traveling Salesman Problem Genetic Algorithms Unsupervised Learning Fuzzy Clustering
Content Type	Text
Resource Type	Article

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