基于混沌粒子群优化的图像相关匹配算法研究

该文将混沌优化搜索技术应用于粒子群优化算法(PSO)，提出了一种基于混沌搜索的粒子群优化算法(CPSO)，该算法利用了PSO算法的收敛快速性和混沌运动的遍历性、随机性等特点，采用混沌初始化粒子初始位置，在运行过程中根据粒子群适应度的方差来自适应混沌更新粒子位置。几种典型函数的测试结果表明:CPSO提高了对多维空间的全局搜索能力，并可以有效避免早熟现象。将该方法用于图像相关匹配算法，提出了一种新的基于CPSO的图像相关匹配算法。实验结果证明该方法对解决噪声情况下的图像匹配问题十分有效。

Research of Image Correlation Matching Method Based on CPSO

A Chaos Particle Swarm Optimization (CPSO) algorithm is presented. The initial location of the particle is evaluated by chaos. During the running time, according to the variance of the population’s fitness, the chaotic location update of the particle is performed adaptively. The experimental results using the testing functions show that CPSO is able to search the global optimizer and avoiding the premature convergence on the multidimensional variable space. Applied the algorithm to image correlation matching, a new image correlation matching method based on CPSO is presented. The experimental results show that this method is very effective for image matching processing with noise.