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Scientific Programming
Volume 2016, Article ID 2148362, 9 pages
http://dx.doi.org/10.1155/2016/2148362
Research Article

Racing Sampling Based Microimmune Optimization Approach Solving Constrained Expected Value Programming

1College of Computer Science, Guizhou University, Guiyang 550025, China
2Department of Big Data Science and Engineering, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China

Received 24 December 2015; Accepted 23 February 2016

Academic Editor: Eduardo Rodríguez-Tello

Copyright © 2016 Kai Yang and Zhuhong Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.