Solving multiobjective flexible scheduling problem by improved DNA genetic algorithm

  • Authors:

  • Jianxiong Li;Shuzhi Nie;Fan Yang

  • Affiliations:

  • School of Software Eng., South China University of Technology and Department of Computer Science and Eng., Guangzhou Vocational & Technical Institute of Industry & Commerce, Guangzhou, Chi ...;School of Mechanical and Automotive Eng., South China University of Technology, Guangzhou, China;School of Computer Science Eng., South China University of Technology and Department of Computer Science and Eng., Guangzhou Vocational & Technical Institute of Industry & Commerce, Guangz ...

  • Venue:
  • CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
  • Year:
  • 2010

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Abstract

Build mathematical models for multi-objective flexible scheduling problems, put forward a improved genetic algorithm based on DNA computation, combine it with Pareto non-dominat ed sorting method to work out multi-objective flexible scheduling optimization problems. In order to ensure the diversity of optimal solution sets, RNA four-digit-system encoder mode and genetic 0 perator based on DNA computation were adopted, designed subs ection crossover and dynamic mutation operation. Through simul ation, test the designed algorithm performance; by comparing wit h conventional genetic algorithm test results, it proved the efficie ncy of the algorithm.