摘要
针对一些求解复杂多峰函数的优化算法的成功率不高的问题,提出了一种基于巴斯卡分布的算法框架。该类算法本质上是并行的,它把已存在的低效算法当成贝努里试验重复执行,直到原低效算法得到两次同样的结果才终止程序。然后,抽象出该算法框架的数学模型,从理论上证明了该类算法能够较大程度地提高原算法的优化成功率,并计算了该类算法相对原算法的时间复杂度的增量。
Some methods,which have been proposed for optimizing complicated multimodal functions,can find out all global optimum of many functions,but probability of success is small.So a new scheme based on Pascal distribution is proposed.In the proposed scheme,a low-efficiency algorithm A which has been proposed is considered as a Bernoulli trial.And the algorithm A is implemented repeatedly until the best result occurs twice.The mathematical model of the new scheme is presented.And it is proved theoretically that the proposed scheme is much more effective than the primary algorithm A for muhimodal function optimization problems.The time complexity＇s increment of the new scheme is calculated.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第21期67-69,共3页
Computer Engineering and Applications
基金
国家重点基础研究发展规划(973)(the National Grand Fundamental Research 973 Program of China under Grant No.2004CCA02500)
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60572015)
湖北省教育厅优秀中青年人才项目(No.Q200726003)。
关键词
巴斯卡分布
贝努里试验
函数优化
差分演化算法
Pascal distribution
Bernoulli trial
function optimization
differential evolution algorithm
