Optimization on Black Box Function Optimization Problem

Jin-ke Xiao, Wei-min Li, Wei Li and Xin-rong Xiao

Mathematical Problems in Engineering, 2015, vol. 2015, 1-10

Abstract:

There are a large number of engineering optimization problems in real world, whose input-output relationships are vague and indistinct. Here, they are called black box function optimization problem (BBFOP). Then, inspired by the mechanism of neuroendocrine system regulating immune system, BP neural network modified immune optimization algorithm (NN-MIA) is proposed. NN-MIA consists of two phases: the first phase is training BP neural network with expected precision to confirm input-output relationship and the other phase is immune optimization phase, whose aim is to search global optima. BP neural network fitting without expected fitting precision could be replaced with polynomial fitting or other fitting methods within expected fitting precision. Experimental simulation confirms global optimization capability of MIA and the practical application of BBFOP optimization method.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2015/647234.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2015/647234.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:647234

DOI: 10.1155/2015/647234

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().