Improved Quantum-behaved Particle Swarm Optimization Algorithm With Disturbance And Its Application

A novel class of evolutionary computation technique-Swarm Intelligence Algorithm is discussed, among which the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is a recently proposed approach and is a variant of original Particle Swarm Optimization (PSO). QPSO is global convergent and will be a promising solver for complex optimization problem, which is shown by some previous work. Thus, the research of this paper will be of somewhat significance in evolutionary computation area.The traditional evolutionary algorithm called Genetic Algorithm (GA) is firstly formulated in particular and in turn the Swarm Intelligence Algorithm including PSO and QPSO. As we known, the most complained problem about evolutionary computation is Premature Convergence (PC), which is also inevitable in QPSO. Thus this work focuses on how to overcome it and consequently proposes enhanced QPSO: an improved QPSO with disturbance.In improved QPSO, an approach of using disturbance for the swarm is proposed to enhance the global search ability of the QPSO algorithm. When implementing the improved QPSO, the whole particle swarm could undertake persistent search in the solution space, leading to an algorithm that can avoid premature convergence effectively. The disturbance is introduced when premature occurs. A mature factor in search process is introduced. And a high bound value is set for the mature factor measure. That means the invalid iteration generations will be maintained lower than the high bound. When the invalid iteration generations exceed the high bound, the disturbance will function. The experiment results on several benchmark function show that the improved QPSO with disturbance method may be a good technique to avoid premature convergence and may result in performance improvement of the QPSO in many cases.The applicability of PSO and QPSO is also explored to image interpolation problems. The image interpolation problem is basically in image processing. It can be modeled by linear minimum mean square-error (LMMSE). PSO and QPSO are used to seek the best high resolution image. The simulation results show that the new interpolation techniques can preserve edge sharpness and reduce artifacts. It has also been shown that QPSO could generate better solutions than PSO on image interpolation problems. So QPSO is good for image interpolation problem.