1. A novel quantum swarm evolutionary algorithm and its applications
- Author
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Yanchun Liang, Wengang Zhou, Xiao-Yue Feng, Chunguang Zhou, Dong-Bing Pu, Yan Wang, and Yanxin Huang
- Subjects
Mathematical optimization ,Heuristic (computer science) ,Cognitive Neuroscience ,Computer Science::Neural and Evolutionary Computation ,Evolutionary algorithm ,Swarm behaviour ,Particle swarm optimization ,Travelling salesman problem ,Computer Science Applications ,Artificial Intelligence ,Search algorithm ,Knapsack problem ,Multi-swarm optimization ,Mathematics - 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.
- Published
- 2007
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