Study and analysis of particle swarm optimization for improving partition clustering
Particle swarm optimization (PSO) algorithms have gained increasing interest for dealing with continuous optimization problems in recent years. Often such problems involve boundary constraints. In this case, one has to cope with the situation that particles may leave the feasible search space. To deal with such situations different bound handling methods have been proposed in the literature and it has been observed that the success of PSO algorithms depends on a large degree on the used bound handling method. In this paper, we propose an alternative approach to cope with bounded search spaces. The idea is to introduce a velocity adaptation mechanism into PSO algorithms that is similar to step size adaptation used in evolution strategies. Using this approach we show that the bound handling method becomes less important for PSO algorithms and that using velocity adaptation leads to better results for a wide range of benchmark functions.