混沌粒子群优化的纹理合成算法研究

粒子群算法在搜索后期由于搜索空间有限，容易陷入局部极值，过早地进入早熟状态。针对这种情况，将混沌优化搜索技术用于粒子群算法，利用混沌运动的通历性、随机性等特点，提出了一种混沌粒子群优化的块采样纹理合成算法。实验结果表明，混沌粒子群算法比粒子群算法具有更好的全局寻优能力，克服了粒子群算法的缺点，得到了较高质量的纹理合成图像。

Algorithm of Texture Synthesis Based on Chaos Particle Swarm Optimization

As the searching space is limited in its later search, the particle swarm optimization algorithm factors is easy to fall into local minimum, and access to the premature state curly. In response to these circumstances, a new patch-based method for texture synthesis based on chaos particle swarm optimization was proposed. Chaos optimization search technique was used in particle swarm optimization in this paper. The experimental result shows that comparing with particle swarm optimization, chaos particle swarm optimization has better optimization performance, overcomes the disadvantage of particle swarm optimization, and gains the texture synthesis image of higher quality.