A new quantum-behaved particle swarm optimization based on cultural evolution mechanism for multiobjective problems

The application of quantum-behaved particle swarm optimization to multiobjective problems has attracted more and more attention recently. However, in order to extend quantum-behaved particle swarm optimization to multiobjective context, two major problems, namely the selection of personal and global best positions and the maintenance of population diversity, need to be taken into consideration. In this paper, a novel Cultural MOQPSO algorithm is proposed, in which cultural evolution mechanism is introduced into quantum-behaved particle swarm optimization to deal with multiobjective problems. In Cultural MOQPSO, the exemplar positions of each particle are obtained according to “belief space,” which contains different types of knowledge. Moreover, to increase population diversity and obtain continuous and even-distributed Pareto fronts, a combination-based update operator is proposed to update the external population in this paper. A comprehensive comparison of Cultural MOQPSO with some state-of-the-art evolutionary algorithms on several benchmark test functions, including ZDT, DTLZ and CEC2009 test instances, demonstrates the effectiveness of the proposed algorithm.