Fractional Particle Swarm Optimization in Multidimensional Search Space

In this paper, we propose two novel techniques, which successfully address several major problems in the field of particle swarm optimization (PSO) and promise a significant breakthrough over complex multimodal optimization problems at high dimensions. The first one, which is the so-called multidimensional (MD) PSO, re-forms the native structure of swarm particles in such a way that they can make interdimensional passes with a dedicated dimensional PSO process. Therefore, in an MD search space, where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. This eventually removes the necessity of setting a fixed dimension a priori, which is a common drawback for the family of swarm optimizers. Nevertheless, MD PSO is still susceptible to premature convergences due to lack of divergence. Among many PSO variants in the literature, none yields a robust solution, particularly over multimodal complex problems at high dimensions. To address this prob...

• Type: Journal
• Year: 2010
• Where: TSMC
• Authors: Serkan Kiranyaz, Turker Ince, E. Alper Yildirim, Moncef Gabbouj
• Keywords: Artificial Intelligence, Dimension, Swarm, Swarm Particles, TSMC 2010