RoughPSO: rough set-based particle swarm optimisation

Particle swarm optimisation (PSO) is an optimisation algorithm based on stochastic search technique. PSO has many similar characteristics with evolutionary computation such as genetic algorithms (GA). Unlike GA, PSO has no evolution operators. In PSO, the particles (potential solutions) fly through the solution space by following the current optimum particles. However, PSO is easy to converge to a local optimum because the search process is stochastic. Rough set, in computer science, is a formal approximation of a conventional set in terms of a pair of sets. Rough set gives the lower and the upper approximation of the original set and is always used to deal with those uncertainty problems. In this paper, the properties of rough set theory are used to improve the local convergence problems in PSO, thereby an algorithm RoughPSO is proposed. RoughPSO utilises the lower- and upper-approximation sets of rough set to obtain the membership values. These values are then used to update the velocity and position of each particle. RoughPSO is applied for function optimisation and classification in machine learning. Empirical study shows that RoughPSO not only can solve the convergence to a local optimum, but also obtains higher classification accuracy rates on some datasets than those PSO-based classification algorithms.