Approximate Bilevel Optimization with Population-Based Evolutionary Algorithms

Kalyanmoy Deb*, Ankur Sinha, Pekka Malo, Zhichao Lu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

Population-based optimization algorithms, such as evolutionary algorithms, have enjoyed a lot of attention in the past three decades in solving challenging search and optimization problems. In this chapter, we discuss recent population-based evolutionary algorithms for solving different types of bilevel optimization problems, as they pose numerous challenges to an optimization algorithm. Evolutionary bilevel optimization (EBO) algorithms are gaining attention due to their flexibility, implicit parallelism, and ability to customize for specific problem solving tasks. Starting with surrogate-based single-objective bilevel optimization problems, we discuss how EBO methods are designed for solving multi-objective bilevel problems. They show promise for handling various practicalities associated with bilevel problem solving. The chapter concludes with results on an agro-economic bilevel problem. The chapter also presents a number of challenging single and multi-objective bilevel optimization test problems, which should encourage further development of more efficient bilevel optimization algorithms.

Original languageEnglish
Title of host publicationSpringer Optimization and Its Applications
Pages361-402
Number of pages42
ISBN (Electronic)978-3-030-52119-6
DOIs
Publication statusPublished - 2020
MoE publication typeA3 Part of a book or another research book

Publication series

NameSpringer Optimization and Its Applications
Volume161
ISSN (Print)1931-6828
ISSN (Electronic)1931-6836

Keywords

  • Approximate optimization
  • Evolutionary algorithms
  • Evolutionary bilevel optimization
  • Metaheuristics

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