Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Performance Analysis of Distributed Genetic Algorithms for Traveling Salesman Problem

순회판매원문제를 위한 분산유전알고리즘 성능평가

  • Kim, Young Nam (School of Information & Computer Engineering, Hongik University) ;
  • Lee, Min Jung (School of Information & Computer Engineering, Hongik University) ;
  • Ha, Chunghun (School of Information & Computer Engineering, Hongik University)
  • 김영남 (홍익대학교 정보컴퓨터공학부 산업공학전공) ;
  • 이민정 (홍익대학교 정보컴퓨터공학부 산업공학전공) ;
  • 하정훈 (홍익대학교 정보컴퓨터공학부 산업공학전공)
  • Received : 2016.09.20
  • Accepted : 2016.12.08
  • Published : 2016.12.31
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Abstract

Distributed genetic algorithm (DGA), also known as island model or coarse-grained model, is a kind of parallel genetic algorithm, in which a population is partitioned into several sub-populations and each of them evolves with its own genetic operators to maintain diversity of individuals. It is known that DGA is superior to conventional genetic algorithm with a single population in terms of solution quality and computation time. Several researches have been conducted to evaluate effects of parameters on GAs, but there is no research work yet that deals with structure of DGA. In this study, we tried to evaluate performance of various genetic algorithms (GAs) for the famous symmetric traveling salesman problems. The considered GAs include a conventional serial GA (SGA) with IGX (Improved Greedy Crossover) and several DGAs with various combinations of crossover operators such as OX (Order Crossover), DPX (Distance Preserving Crossover), GX (Greedy Crossover), and IGX. Two distinct immigration policies, conventional noncompetitive policy and newly proposed competitive policy are also considered. To compare performance of GAs clearly, a series of analysis of variance (ANOVA) is conducted for several scenarios. The experimental results and ANOVAs show that DGAs outperform SGA in terms of computation time, while the solution quality is statistically the same. The most effective crossover operators are revealed as IGX and DPX, especially IGX is outstanding to improve solution quality regardless of type of GAs. In the perspective of immigration policy, the proposed competitive policy is slightly superior to the conventional policy when the problem size is large.

Keywords

References

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