A novel quantum swarm evolutionary algorithm and its applications

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...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2007, Vol.70 (4), p.633-640
Main Authors: Wang, Yan, Feng, Xiao-Yue, Huang, Yan-Xin, Pu, Dong-Bing, Zhou, Wen-Gang, Liang, Yan-Chun, Zhou, Chun-Guang
Format: Article
Language:English
Subjects:
Knapsack problem
Particle swarm optimization
Quantum swarm evolutionary algorithm
Quantum-inspired evolutionary algorithm
Traveling salesman problem
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Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2006.10.001