A Hybrid Quantum-Inspired Evolutionary Algorithm for Capacitated Vehicle Routing Problem

  • Authors:

  • Jing-Ling Zhang;Yan-Wei Zhao;Dian-Jun Peng;Wan-Liang Wang

  • Affiliations:

  • Key Laboratory of Mechanical Manufacture and Automation of Ministry of Education, Zhejiang Univ. of Tech, Hangzhou, China 310032;Key Laboratory of Mechanical Manufacture and Automation of Ministry of Education, Zhejiang Univ. of Tech, Hangzhou, China 310032;Key Laboratory of Mechanical Manufacture and Automation of Ministry of Education, Zhejiang Univ. of Tech, Hangzhou, China 310032;College of Information Engineering, Zhejiang Univ. of Tech, Hangzhou, China 310032

  • Venue:
  • ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
  • Year:
  • 2008

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Abstract

A hybrid Quantum-Inspired Evolutionary Algorithm (HQEA) with 2-OPT sub-routes optimization for capacitated vehicle routing problem (CVRP) is proposed. In the HQEA, 2-OPT algorithm is used to optimize sub-routes for convergence acceleration. Moreover, an encoding method of converting Q-bit representation to integer representation is designed. And genetic operators of quantum crossover and quantum variation are applied to enhance exploration. The proposed HQEA is tested based on classical benchmark problems of CVRP. Simulation results and comparisons with genetic algorithm show that the proposed HQEA has much better exploration quality and it is an effective method for CVRP.