Journal of Computer Applications (计算机应用) 2013/33:12 PP.3375-3379
To keep the diversity of particles when multi-objective particle swarm optimization is running, a multi-objective optimization algorithm was proposed based on particle swarms initialization and dynamic multiple particle swarms cooperation. The quantity of swarms was increased or decreased dynamically according to the distribution of particle swarms in the decision space. To avoid converging too quickly, the factors, which affected the flying speed of a particle, were improved to depend on the current velocity inertia of the particle, the best value of the particle, the best value of the swarm which the particle belonged to, and the optimal value of all swarms. This algorithm was tested by five benchmark functions and compared with the multi-objective particle swarm optimization. The experimental results indicate that the proposed algorithm is superior to the multi-objective particle swarm optimization.