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A Fast Parallel Genetic Algorithm for Traveling Salesman Problem

  • Chun-Wei Tsai
  • Shih-Pang Tseng
  • Ming-Chao Chiang
  • Chu-Sing Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6083)

Abstract

In this paper, we present a fast scalable method to reduce the computation time of genetic algorithms for traveling salesman problem, called the Parallel Pattern Reduction Enhanced Genetic Algorithm (PPREGA). The general idea behind the proposed algorithm is twofold: (1) Eliminate the redundant computations of GA on its convergence process by pattern reduction and (2) Minimize the completion time of GA by parallel computing. Our simulation result shows that the proposed algorithm can significantly reduce not only the computation time but also the maximum completion time of GA. Moreover, our simulation result shows further that the loss of the quality of the end result is small.

Keywords

Genetic Algorithm Computation Time Local Search Travel Salesman Problem Common Gene 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Chun-Wei Tsai
    • 1
    • 2
  • Shih-Pang Tseng
    • 1
    • 3
  • Ming-Chao Chiang
    • 1
  • Chu-Sing Yang
    • 2
  1. 1.Computer Science and EngineeringNational Sun Yat-sen UniversityKaohsiungTaiwan
  2. 2.Electrical EngineeringNational Cheng Kung UniversityTainanTaiwan
  3. 3.Computer Science and Information EngineeringTajen UniversityPingtungTaiwan

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