A quantum-inspired bacterial swarming optimization algorithm for discrete optimization problems

Authors:
Jinlong Cao;Hongyuan Gao
Affiliations:
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China;College of Information and Communication Engineering, Harbin Engineering University, Harbin, China
Venue:
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2012

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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.