In order to improve the processing performance of the multi-objective optimization problem,reduce the computational complexity and improve the convergence of the algorithm, a multi-objective particle swarm optimization algo- rithm based on a human social behavior was proposed. The strategies such as promotion/resistance factor and the local jump strategy are introduced in proposed algorithm, to make the algorithm have strong global search ability and good robust performance. Some typical multi-objective optimization functions were tested to verify the algorithm. The results show that the propo...
