An advanced Quantum-behaved Particle Swarm Optimization algorithm utilizing cooperative strategy

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.