A quantum-behaved particle swarm optimization(CLQPSO) algorithm based on comprehensive learning strategy
is presented, which helps prevent the original quantum-behaved particle swarm optimization(QPSO) algorithm’s tendency to be easily trapped into local optima as a result of the rapid decline in diversity. The learning strategy changes the updating method of local attractor in QPSO, which makes fully use of the social information of the swarm. The 8 benchmark functions are used to test the performance of CLQPSO. The experiments results show that the proposed algorithm can find better solutions than the original QPSO algorithm and the PSO algorithm with higher efficiency.