Evolutionary algorithm for bilevel optimization using approximations of the lower level optimal solution mapping

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • Michigan State University

Abstract

Bilevel optimization problems are a class of challenging optimization problems, which contain two levels of optimization tasks. In these problems, the optimal solutions to the lower level problem become possible feasible candidates to the upper level problem. Such a requirement makes the optimization problem difficult to solve, and has kept the researchers busy towards devising methodologies, which can efficiently handle the problem. Despite the efforts, there hardly exists any effective methodology, which is capable of handling a complex bilevel problem. In this paper, we introduce bilevel evolutionary algorithm based on quadratic approximations (BLEAQ) of optimal lower level variables with respect to the upper level variables. The approach is capable of handling bilevel problems with different kinds of complexities in relatively smaller number of function evaluations. Ideas from classical optimization have been hybridized with evolutionary methods to generate an efficient optimization algorithm for a wide class of bilevel problems. The performance of the algorithm has been evaluated on two sets of test problems. The first set is a recently proposed SMD test set, which contains problems with controllable complexities, and the second set contains standard test problems collected from the literature. The proposed method has been compared against three benchmarks, and the performance gain is observed to be significant. The codes related to the paper may be accessed from the website http://bilevel.org.

Details

Original languageEnglish
Pages (from-to)395-411
Number of pages17
JournalEuropean Journal of Operational Research
Volume257
Issue number2
Publication statusPublished - 1 Mar 2017
MoE publication typeA1 Journal article-refereed

    Research areas

  • Bilevel optimization, Evolutionary algorithms, Quadratic approximations

ID: 9432647