A Quantum-inspired Bacterial Swarming Optimization Algorithm for Discrete Optimization Problems

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• 作者：Jinlong Cao (1) lansebolang2008@163.com
  Hongyuan Gao (2) gaohongyuan@hrbeu.edu.cn
• 关键词：quantum ; inspired bacterial swarming optimization – ; bacterial foraging optimization – ; particle swarm optimization
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7331
• 期：1
• 页码：29-36
• 全文大小：261.4 KB
• 参考文献：1. Kennedy, J., Eberhart, J.: Discrete binary version of the particle swarm optimization. In: Proc. IEEE International Conference on Computational Cybernetics and Simulation, pp. 4104–4108 (1997)
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• 作者单位：1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China2. College of Information and Communication Engineering, Harbin Engineering University, Harbin, China
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

In order to solve discrete optimization problem, this paper proposes a quantum-inspired bacterial swarming optimization (QBSO) algorithm based on bacterial foraging optimization (BFO). The proposed QBSO algorithm applies the quantum computing theory to bacterial foraging optimization, and thus has the advantages of both quantum computing theory and bacterial foraging optimization. Also, we use the swarming pattern of birds in block introduced in particle swarm optimization (PSO). Then we evaluate the efficiency of the proposed QBSO algorithm through four classical benchmark functions. Simulation results show that the designed algorithm is superior to some previous intelligence algorithms in both convergence rate and convergence accuracy.