基于云模型的全局最优化算法

基于云模型在定性概念与其定量数值表示之间转换过程中的优良特性,结合遗传算法的基本思想,提出一种自适应高精度快速随机搜索算法,并将之运用到函数寻优中.在定性知识的指导下该算法能够自适应控制搜索空间的范围,较好地避免了传统遗传算法易陷入局部最优解和选择压力过大造成的早熟收敛等问题.算法易于实现,不存在遗传算法中的编码问题.试验结果表明该算法具有精度高、收敛速度快等优点.在众多优化问题上有广泛的应用前景. 

Cloud model based algorithm for global optimization of functions

Numerical optimization of given objective functions is a crucial task in many scientific problems.Based on the outstanding characteristics of cloud model on the process of transforming a qualitative concept to a set of quantitative numerical values,and integrate with the basic principle of genetic algorithm,a novel adaptive evolutionary algorithm for continuous global optimization problems was proposed.With the instructions of qualitative knowledge,the extent of searching space is self-adjusted and the possibility of premature and the probability of trapping in local best optimization are greatly reduced,so the algorithm can find high accurate numerical solution within a short time.The algorithm avoids the process of coding and crossover so it is easy to be carried out.By the experiments on typical test functions,the precision,stability and convergence rate were well proved.