An efficient genetic algorithm with fuzzy c-means clustering for traveling salesman problem

Jong Won Yoon, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Genetic algorithms (GA) are one of effective approaches to solve the traveling salesman problem (TSP). When applying GA to the TSP, it is necessary to use a large number of individuals in order to increase the chance of finding optimal solutions. However, this incurs high evaluation costs which make it difficult to obtain fitness values of all the individuals. To overcome this limitation we propose an efficient genetic algorithm based on fuzzy clustering which reduces evaluation costs with minimizing loss of performance. It works by evaluating only one representative individual for each cluster of a given population, and estimating the fitness values of the others from the representatives indirectly. A fuzzy c-means algorithm is used for grouping the individuals and the fitness of each individual is estimated according to membership values. The experiments were conducted with randomly generated cities, and the performance of the method was evaluated by comparing to other GAs. The results showed the usefulness of the proposed method on the TSP.

Original languageEnglish
Title of host publication2011 IEEE Congress of Evolutionary Computation, CEC 2011
Pages1452-1456
Number of pages5
DOIs
Publication statusPublished - 2011 Aug 29
Event2011 IEEE Congress of Evolutionary Computation, CEC 2011 - New Orleans, LA, United States
Duration: 2011 Jun 52011 Jun 8

Publication series

Name2011 IEEE Congress of Evolutionary Computation, CEC 2011

Other

Other2011 IEEE Congress of Evolutionary Computation, CEC 2011
CountryUnited States
CityNew Orleans, LA
Period11/6/511/6/8

Fingerprint

Fuzzy C-means Clustering
Traveling salesman problem
Travelling salesman problems
Fitness
Efficient Algorithms
Genetic algorithms
Genetic Algorithm
Fuzzy C-means Algorithm
Fuzzy clustering
Fuzzy Clustering
Evaluation
Costs
Grouping
Optimal Solution
Necessary
Experiment
Experiments

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Yoon, J. W., & Cho, S. B. (2011). An efficient genetic algorithm with fuzzy c-means clustering for traveling salesman problem. In 2011 IEEE Congress of Evolutionary Computation, CEC 2011 (pp. 1452-1456). [5949786] (2011 IEEE Congress of Evolutionary Computation, CEC 2011). https://doi.org/10.1109/CEC.2011.5949786
Yoon, Jong Won ; Cho, Sung Bae. / An efficient genetic algorithm with fuzzy c-means clustering for traveling salesman problem. 2011 IEEE Congress of Evolutionary Computation, CEC 2011. 2011. pp. 1452-1456 (2011 IEEE Congress of Evolutionary Computation, CEC 2011).
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Yoon, JW & Cho, SB 2011, An efficient genetic algorithm with fuzzy c-means clustering for traveling salesman problem. in 2011 IEEE Congress of Evolutionary Computation, CEC 2011., 5949786, 2011 IEEE Congress of Evolutionary Computation, CEC 2011, pp. 1452-1456, 2011 IEEE Congress of Evolutionary Computation, CEC 2011, New Orleans, LA, United States, 11/6/5. https://doi.org/10.1109/CEC.2011.5949786

An efficient genetic algorithm with fuzzy c-means clustering for traveling salesman problem. / Yoon, Jong Won; Cho, Sung Bae.

2011 IEEE Congress of Evolutionary Computation, CEC 2011. 2011. p. 1452-1456 5949786 (2011 IEEE Congress of Evolutionary Computation, CEC 2011).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Yoon JW, Cho SB. An efficient genetic algorithm with fuzzy c-means clustering for traveling salesman problem. In 2011 IEEE Congress of Evolutionary Computation, CEC 2011. 2011. p. 1452-1456. 5949786. (2011 IEEE Congress of Evolutionary Computation, CEC 2011). https://doi.org/10.1109/CEC.2011.5949786