An efficient global optimization algorithm for mixed-integer nonlinear fractional programs with separable concave terms

Jian Gong, Fengqi You*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

It could be a very challenging task to globally optimize large-scale mixed-integer fractional programs (MIFP) with separable concave and fractional terms in the objective function. To address this computational challenge, we propose a novel and efficient global optimization algorithm, which integrates an inexact parametric algorithm based on Newton's method and a successive piecewise linear approximation algorithm. To demonstrate the efficiency of this algorithm, we use it to optimize the economic and environmental performance of a manufacturing process for biodiesel and bioproducts from microalgae. The problem is solved with several global optimization methods. Computational results show that the proposed global optimization algorithm is more efficient than general-purpose MINLP solvers when solving the special type of MIFP problems.

Original languageEnglish (US)
Title of host publicationACC 2015 - 2015 American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages547-552
Number of pages6
Volume2015-July
ISBN (Electronic)9781479986842
DOIs
StatePublished - Jan 1 2015
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: Jul 1 2015Jul 3 2015

Other

Other2015 American Control Conference, ACC 2015
CountryUnited States
CityChicago
Period7/1/157/3/15

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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