文章基本信息

标题：Racing Sampling Based Microimmune Optimization Approach Solving Constrained Expected Value Programming
本地全文：下载
作者：Kai Yang ; Zhuhong Zhang
期刊名称：Scientific Programming
印刷版ISSN：1058-9244
出版年度：2016
卷号：2016
DOI：10.1155/2016/2148362
出版社：Hindawi Publishing Corporation
摘要：This work investigates a bioinspired microimmune optimization algorithm to solve a general kind of single-objective nonlinear constrained expected value programming without any prior distribution. In the study of algorithm, two lower bound sample estimates of random variables are theoretically developed to estimate the empirical values of individuals. Two adaptive racing sampling schemes are designed to identify those competitive individuals in a given population, by which high-quality individuals can obtain large sampling size. An immune evolutionary mechanism, along with a local search approach, is constructed to evolve the current population. The comparative experiments have showed that the proposed algorithm can effectively solve higher-dimensional benchmark problems and is of potential for further applications.