A novel quantum swarm evolutionary algorithm and its applications

  • Authors:

  • Yan Wang;Xiao-Yue Feng;Yan-Xin Huang;Dong-Bing Pu;Wen-Gang Zhou;Yan-Chun Liang;Chun-Guang Zhou

  • Affiliations:

  • College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry, Changchun 130021, China;College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry, Changchun 130021, China;College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry, Changchun 130021, China;Department of Computer Science, Northeast Normal University, Changchun 130024, China;College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry, Changchun 130021, China;College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry, Changchun 130021, China;College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry, Changchun 130021, China

  • Venue:
  • Neurocomputing
  • Year:
  • 2007

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Abstract

In this paper, a novel quantum swarm evolutionary algorithm (QSE) is presented based on the quantum-inspired evolutionary algorithm (QEA). A new definition of Q-bit expression called quantum angle is proposed, and an improved particle swarm optimization (PSO) is employed to update the quantum angles automatically. The simulated results in solving 0-1 knapsack problem show that QSE is superior to traditional QEA. In addition, the comparison experiments show that QSE is better than many traditional heuristic algorithms, such as climb hill algorithm, simulation anneal algorithm and taboo search algorithm. Meanwhile, the experimental results of 14 cities traveling salesman problem (TSP) show that it is feasible and effective for small-scale TSPs, which indicates a promising novel approach for solving TSPs.