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In this paper, Quantum-behaved Particle Swarm Optimization algorithm (QPSO) is investigated from the perspective of Estimation of Distribution Algorithms (EDAs) for the first time, which proves that QPSO is a combination of EDA and Standard Particle Swarm Optimization algorithm (SPSO). Additionally, a novel cooperative quantum-behaved particle swarm optimization algorithm (CQPSO) is proposed to prevent the Evolutionary Algorithms' universal tendency of premature convergence as a result of rapid decline in diversity. It is a type of parallel algorithm in which several QPSO algorithms are simulated individually in sub-swarms with frequent recombination which plays a roll of message passing. The most effective settings of Communication Frequency and the Size of Each Sub-Swarm for this novel algorithm are studied through experiments. Our experiments also show that CQPSO is able to find better solutions than the original QPSO and SPSO with higher efficiency.
Date of Conference: 25-27 Aug. 2010