Sample Bound Estimate Based Chance-constrained Immune Optimization and Its Applications  

作　　者：Zhu-Hong Zhang Kai Yang Da-Min Zhang 

机构地区：[1]College of Big Data & Information Engineering, Guizhou University [2]College of Computer Science & Technology, Guizhou University

出　　处：《International Journal of Automation and computing》2016年第5期468-479,共12页国际自动化与计算杂志（英文版）

基　　金：supported in part by National Natural Science Foundation of China(Nos.61563009 and 61065010);Doctoral Fund of Ministry of Education of China(No.20125201110003)

摘　　要：This work investigates a simple and practical bio-immune optimization approach to solve a kind of chance-constrained programming problem without known noisy attributes, after probing into a lower bound estimate of sample size for any random variable. Such approach mainly consists of sample allocation, evaluation, proliferation and mutation. The former two, depending on a lower bound estimate acquired, not only decide the sample size of random variable and the importance level of each evolving B cell, but also ensure that such B cell is evaluated with low computational cost; the third makes diverse B cells participate in evolution and suppresses the influence of noise; the last, which associates with the information on population diversity and fitness inheritance, creates diverse and high-affinity B cells. Under such approach, three similar immune algorithms are derived after selecting different mutation rules. The experiments, by comparison against two valuable genetic algorithms, have illustrated that these immune algorithms are competitive optimizers capable of effectively executing noisy compensation and searching for the desired optimal reliable solution.This work investigates a simple and practical bio-immune optimization approach to solve a kind of chance-constrained programming problem without known noisy attributes, after probing into a lower bound estimate of sample size for any random variable. Such approach mainly consists of sample allocation, evaluation, proliferation and mutation. The former two, depending on a lower bound estimate acquired, not only decide the sample size of random variable and the importance level of each evolving B cell, but also ensure that such B cell is evaluated with low computational cost; the third makes diverse B cells participate in evolution and suppresses the influence of noise; the last, which associates with the information on population diversity and fitness inheritance, creates diverse and high-affinity B cells. Under such approach, three similar immune algorithms are derived after selecting different mutation rules. The experiments, by comparison against two valuable genetic algorithms, have illustrated that these immune algorithms are competitive optimizers capable of effectively executing noisy compensation and searching for the desired optimal reliable solution.

关 键 词：Chance-constrained programming immune optimization sample allocation lower bound estimate noise attenuation 

分 类 号：Q811.4[生物学—生物工程]