A novel quantum-behaved particle swarm optimization with random selection for large scale optimization

Large scale optimization has become a well-recognised field in many science and engineering applications and a variety of metaheuristic algorithms adopting cooperative coevolution (CC) framework with problem decomposition have been applied to solve them. In this paper, a novel decomposition strategy termed as random selection is proposed. In random selection strategy, only a small part of decision variables are randomly selected to form a group for evolving at every iteration and the maximum number of randomly selected decision variables are limited by the parameter RSSCALE. By random selection, the randomly selected searching subspace is explored sufficiently in each iteration and the whole search space can be fully covered after several iterations. We evaluate the random selection strategy by combining quantum-behaved particle swarm optimization (RSQPSO) and a comparative study is carried out on a set of benchmark functions between RSQPSO and four state-of-the-art algorithms, which were specially designed for large scale optimization. The comparative results show that the proposed approach performs well for solving large scale optimization problems.