Multi-population parallel genetic algorithm using a new genetic representation for the euclidean traveling salesman problem
Author
Kapanoğlu, M.
Koç, İ. O.
Kara, İ.
Aktürk, Mehmet Selim
Date
2005Source Title
Proceedings of the 35th International Conference on Computers and Industrial Engineering, ICC and IE 2005
Publisher
İstanbul Technical University
Pages
1047 - 1052
Language
English
Type
Conference PaperItem Usage Stats
79
views
views
10
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downloads
Abstract
This paper introduces a multi-population genetic algorithm (M-PPGA) using a new genetic representation, the kth-nearest neighbor representation, for Euclidean Traveling Salesman Problems. The proposed M-PPGA runs M greedy genetic algorithms on M separate populations, each with two new operators, intersection repairing and cheapest insert. The M-PPGA finds optimal or near optimal solutions by using a novel communication operator among individually converged populations. The algorithm generates high quality building blocks within each population; then, combines these blocks to build the optimal or near optimal solutions by means of the communication operator. The proposed M-PPGA outperforms the GAs that we know of as competitive with respect to running times and solution quality, over the considered test problems including the Turkey81.
Keywords
Kth-nearest neighbor representationMulti-population genetic algorithm
Traveling salesman problem
Building blockes
Genetic representations
Multi population
Near-optimal solutions
Nearest neighbors
Parallel genetic algorithms
Solution quality
Genetic algorithms
Industrial engineering
Optimal systems
Permalink
http://hdl.handle.net/11693/27343Collections
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