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 EDAs and original
particle swarm optimization. A quantum-behaved particle swarm optimization algorithm based on cooperative search strategy
is presented, which helps prevent the evolutionary algorithms’ universal tendency to be easily trapped into local optima as
a result of the rapid decline in diversity. Communication frequency and the size of each sub-swarm are ensured through
experiments to obtain the most effective setting for this algorithm. Experiment results show that this algorithm is able to find
better solutions than the original QPSO and particle swarm optimization algorithm with higher efficiency.