Cooperative Quantum-behaved Particle Swarm Optimization And Its Applications

The swarm intelligence algorithm comes down to simulating the group behavior of life in nature, and it is one kind of optimization algorithm of random searching, which includes Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). Particle Swarm Optimization is an evolution optimization algorithm proposed by Kennedy and Eberhart in1995. Particle Swarm Optimization system simulates the knowledge evolvement of a social organism. In this system, every particle represents a candidate solution to the problem at hand. The particles fly through a multidimensional search space to find out the optima or sub-optima. Particles fly according to the present position and the present speed. In other words, the particles fly along a fixed path to search in its neighbor. Particle Swarm Optimization algorithm is widely applied because of its simple concept and easy to adjust parameters, and at the same time many improved algorithms appear. However, it is hard to jump from the local optimal position because of the fixed trajectory and the limited speed and easy to fall into precocity. The appearance of Quantum-behaved Particle Swarm Optimization (QPSO) overcomes the problem of the limited search space. The QPSO is based on the indefinite principle of quantum mechanics and its global search ability is better than PSO algorithm. But QPSO is not a perfect algorithm, many improve QPSO appearing. These all algorithm have the disadvantage of high computing complexity and poor global search ability. This paper introduces and analysis the Particle Swarm Optimization and quantum-behaved Particle Swarm Optimization, and proposed improved algorithms and applied the improved algorithms in medicine image imagetation. The main work of this paper is following:1) An improved cooperative quantum-behaved particle swarm optimization algorithm is proposed to overcome the drawback of existing particle swarm optimization algorithm. The algorithm takes full advantage of quantum particles in the uncertainty principle study. The particles are measured several times during the update process and collaborated on effective use more information to accelerate the convergence rate and reduce the time complexity. Comparative experiments show that this method accelerates the convergence rate and has a good performance on the benchmark functions, especially for high-dimensional function.2) An image segmentation method based on the improved cooperative quantum-behaved particle swarm optimization algorithm is proposed. We improved this algorithm by combining the cooperative quantum-behaved particle swarm strategy with the maximizing class distance variance method. Comparative experiment results show that the improved algorithm has better performance and effect in image segmentation superior to others under the same condition, and the precision of segmentation is enhanced3) A multi-context variables cooperative quantum-behaved particle swarm optimization algorithm is proposed. This algorithm updates the context vector when the present context vector completes the cooperation with a particle. Comparative experiment results show that search ability is enhanced and the optimized result is more ideal. We also combine this algorithm with OTST method for image segmentation and proposed an image segmentation method based on a multi-context variables cooperative quantum-behaved particle swarm optimization algorithm. Comparative experiment results show that the performance of the proposed approach in both evaluation indexes and visual quality.This research is supported by the National High Technology Research and Development Program (863Program) of China (Grant No.2009AA12Z210), the Key Scientific and Technological Innovation Special Projects of Shaanxi "13115",(No.2008ZDKG-37), the National Natural Science Foundation of China(Grant No.60703107and60703108),the Natural Science Basic Research Plan in Shaanxi Province of China(Grant No.2007F32), the China Postdoctoral Science Foundation Special funded project (No.200801426), the China Postdoctoral Science Foundation funded project (No.20080431228) and the Fundamental Research Funds for the Central Universities (No.JY10000902040).