A new MOEA for multi-objective TSP and Its convergence property analysis

  • Authors:

  • Zhenyu Yan;Linghai Zhang;Lishan Kang;Guangming Lin

  • Affiliations:

  • State Key Laboratory of Software Engineering, Wuhan University, Wuhan, P. R. China;Department of Computer Science, York University, Toronto, Ontario, Canada;State Key Laboratory of Software Engineering, Wuhan University, Wuhan, P. R. China;School of Computer Science, UC, UNSW Australian Defence Force Academy, Canberra, ACT, Australia and Capital Bridge Securities Co., Ltd, Shanghai, P. R. China

  • Venue:
  • EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

Evolutionary Multi-objective Optimization(EMO) is becoming a hot research area and quite a few aspects of Multi-objective Evolutionary algorithms(MOEAs) have been studied and discussed. However there are still few literatures discussing the roles of search and selection operators in MOEAs. This paper studied their roles by a representative combinatorial Multi-objective Problem(MOP): Multi-objective TSP. In the new MOEA, We adopt an efficient search operator, which has the properties of both crossover and mutation, to generate the new individuals and chose two kinds of selection operators: Family Competition and Population Competition with probabilities to realize selection. The simulation experiments showed that this new MOEA could get good uniform solutions representing the Pareto Front and outperformed SPEA in almost every simulation run on this problem. Furthermore, we analyzed its convergence property using Finite Markov Chain and proved that it could converge to Pareto Front with probability 1.We also find that the convergence property of MOEAs has much relationship with search and selection operators.