A Novel Multiobjective Quantum-Behaved Particle Swarm Optimization Based on the Ring Model

Due to its fast convergence and population-based nature, particle swarm optimization (PSO) has been widely applied to address themultiobjective optimization problems (MOPs). However, the classical PSO has been proved to be not a global search algorithm. Therefore, there may exist the problem of not being able to converge to global optima in the multiobjective PSO-based algorithms. In this paper, making full use of the global convergence property of quantum-behaved particle swarm optimization (QPSO), a novel multiobjective QPSO algorithm based on the ring model is proposed. Based on the ring model, the position-update strategy is improved to addressMOPs. The employment of a novel communicationmechanism between particles effectively slows down the descent speed of the swarmdiversity. Moreover, the searching ability is further improved by adjusting the position of local attractor. Experiment results show that the proposed algorithmis highly competitive on both convergence and diversity in solving theMOPs. In addition, the advantage becomes even more obvious with the number of objectives increasing.