A New Granular Particle Swarm Optimization Variant for Granular Optimization Problems

As an emerging computing paradigm of information processing, Granular Computing exhibits great potential in human-centric decision problems such as feature selection and feature extraction, pattern recognition and knowledge discovery. Optimization plays an important role in these areas. The optimization problems arising in Granular Computing area are called granular optimization problems in which information granules are treated as information processing units and therefore granules denote the related solutions. Particle swarm optimization (PSO) has been demonstrated to be a very competitive algorithm in solving global optimization problems. In this paper, we develop a novel PSO variant called granular PSO to solve problems of granular optimization. Each granule in this study is expressed as a multidimension hyper-box with each coordinate being described by an interval. In the proposed granular PSO, the velocity and position of a particle is represented by intervals rather than single numerical values. The velocity and position update strategy is modified accordingly. In granular PSO, the solution space search behavior of a particle is realized in granule-to-granule manner rather than point-to-point format. We provide experimental simulations to demonstrate the effectiveness of the proposed granular PSO algorithm.