改进的粒子群算法求解蛋白质结构预测问题

摘要

The biological functions of protein are determined by their dimensional folding structures, and protein structure prediction remains one of the most challenging problems in bioinformatics research. Particle swarm optimization (PSO) algorithm is a new group intelligent algorithm. The advantage of it is simple and easy to achieve .profoundly intelligent backgroud. In the optimization field, PSO is suitable for continuous optimization,and protein structure prediction based on 2D HP lattice model is a discrete problem. Therefore, the concepts of adjustment operator and adjustment sequence is introduced to reconstruct PSO by using the ideas of node regulation algorithm. It is proposed to solve the typical discrete problem. The Numerical simulation results indicate that the algorithm is effective.%蛋白质的生物学功能是由其空间结构决定的,因此,蛋白质结构预测就成为生物信息学领域中极具挑战性的问题之一.粒子群算法是一种新的群智能算法,优势在于简单容易实现,又有深刻的智能背景.在优化领域,粒子群算法适用 于求解连续优化问题,而基于HP格点模型的蛋白质结构预测问题是一个离散问题.因此,文中通过借鉴单点调整算法的思想,引入了调整子和调整序的概念,重构了粒子群算法,并用改进的粒子群算法求解了这一典型的离散问题.数值模拟结果说明了算法的有效性.