量子行为微粒群算法及其应用

Algorithm and Application of Quantum-behaved Particle Swarm Optimization

将量子行为的微粒群(Quantum-behaved Particle Swarm Optimization即QPSO)算法和图像融合相结合,提出了基于QPSO算法的图像融合算法,将图像融合问题归结为最优化问题。实验表明,在图像融合中,QPSO算法可以很快地得到最优值,与遗传算法以及PSO算法相比参数较少,在取得良好的融合效果的同时,运用算法的并行搜索机制显著地提高了融合速度。

Combining quantum-behaved particle swarm optimization with image fusion,the paper put forward image fusion algorithm which may boil down to the optimization problem.The experiment indicates that in image fusion,QPSO algorithm can get the optimal value quickly and has fewer parameters compared to genetic algorithm and PSO algorithm.While achieving good fusion effect,application of the parallel search mechanism has obviously improve fusion speed.