混合群智能算法在模体识别中的应用

摘要

In order to avoid many Gibbs algorithm defects, improve the ability of problem solving, improvements the ACO (Ant Colony Optimization): PSO (Particle Swarm Optimization) is made to optimize the parameters in the ACO. Pentium PC machine is the experiment platform, operating system is Windows 2003 Server, development tools is VB,the traveling salesman problem is tsimalated. Results show that the computing time of the algorithm can be reduced by new methods. It had great effects in practicality and rapid processing of motif discovary.%为了避免传统吉布斯算法的诸多缺陷,提高算法的求解能力,对蚁群算法(ACO:Ant Colony Optimization)进行了改进:引入粒子群算法(PSO:Particle Swarm Optimization)动态调节ACO函数中的参数获得最优解.在奔腾PC机的实验平台上、Windows 2003 Server操作系统下,开发工具为VB的模拟实验中,结果证明,混合的群智能算法使经典旅行商问题求解的计算时间缩短,提高了算法的收敛速度,有较好的发展前景.利用PSO处理连续优化问题的优点,将混合算法应用于生物信息学的模体识别中,可实现更加快速的基序发现处理.