An improved partheno-genetic algorithm for the multi-constrained problem of curling match arrangement

Rui Ding, Hongbin Dong, Jun He, Yuxin Dong

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

Curling-match arrangement is a multi-constrained optimization problem in the real world. An improved partheno-genetic algorithm is used for solving the problem in this paper. In order to handle the complicated relationships among the particular constraints in curling-match, an eliminate-selection strategy is proposed to increase population diversity. Two genetic operators, targeted self-crossover operator and fixed-random self-crossover operator, are designed to ensure that the algorithm can convergence rapidly. With bi-level optimization, the improved partheno-genetic algorithm enhances its search ability. An orthogonal method is used to obtain the algorithm parameters. Simulation results demonstrate that the improved algorithm can solve the curling-match multi-constrained optimization problem efficiently.

Original languageEnglish
Title of host publication2016 IEEE Congress on Evolutionary Computation, CEC 2016
PublisherIEEE Press
Pages957-964
Number of pages8
ISBN (Electronic)9781509006229
DOIs
Publication statusPublished - 14 Nov 2016
Event2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Publication series

Name2016 IEEE Congress on Evolutionary Computation, CEC 2016

Conference

Conference2016 IEEE Congress on Evolutionary Computation, CEC 2016
Country/TerritoryCanada
CityVancouver
Period24 Jul 201629 Jul 2016

Keywords

  • Curling Match Arrangement
  • Eliminate-selection Strategy
  • Fixed-Random Self-Crossover
  • Partheno-Genetic Algorithm
  • Targeted Self-Crossover

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