Abstract
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.
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Cao, J., Gao, H. (2012). A Quantum-inspired Bacterial Swarming Optimization Algorithm for Discrete Optimization Problems. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_4
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DOI: https://doi.org/10.1007/978-3-642-30976-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30975-5
Online ISBN: 978-3-642-30976-2
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