DocumentCode :
2911293
Title :
Discrete quantum-behaved particle swarm optimization based on estimation of distribution for combinatorial optimization
Author :
Wang, Jiahai ; Zhang, Yunong ; Zhou, Yalan ; Yin, Jian
Author_Institution :
Dept. of Comput. Sci., Sun Yatsen Univ., Guangzhou
fYear :
2008
fDate :
1-6 June 2008
Firstpage :
897
Lastpage :
904
Abstract :
Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm. A quantum-behaved particle swarm optimization (QPSO) is also proposed by combining the classical PSO philosophy and quantum mechanics. These algorithms have been very successful in solving the global continuous optimization, but their applications to combinatorial optimization have been rather limited. Estimation of distribution algorithm (EDA) samples new solutions from a probability model which characterizes the distribution of promising solutions. This paper proposes a novel discrete QPSO based on EDA for the combinatorial optimization problem. The proposed algorithm combines global statistical information extracted by EDA with local information obtained by discrete QPSO to create promising solutions. To demonstrate the performance of the proposed algorithm, experiments are carried out on the unconstrained binary quadratic programming problem which numerous hard combinatorial optimization problems can be formulated as. The results show that the discrete QPSO based on EDA have superior performance to other algorithms.
Keywords :
combinatorial mathematics; particle swarm optimisation; quadratic programming; quantum theory; combinatorial optimization; discrete quantum-behaved particle swarm optimization; estimation of distribution algorithm; global statistical information; population-based swarm intelligence algorithm; quantum mechanics; unconstrained binary quadratic programming problem; Birds; Clustering algorithms; Convergence; Data mining; Electronic design automation and methodology; Genetics; Marine animals; Particle swarm optimization; Probability; Quantum mechanics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
Type :
conf
DOI :
10.1109/CEC.2008.4630902
Filename :
4630902
Link To Document :
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