Approximate Bilevel Optimization with Population-Based Evolutionary Algorithms

Kalyanmoy Deb (), Ankur Sinha (), Pekka Malo () and Zhichao Lu ()
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Kalyanmoy Deb: Michigan State University
Ankur Sinha: Indian Institute of Management
Pekka Malo: Aalto University School of Business
Zhichao Lu: Michigan State University

Chapter Chapter 13 in Bilevel Optimization, 2020, pp 361-402 from Springer

Abstract: 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.

Keywords: Evolutionary algorithms; Metaheuristics; Evolutionary bilevel optimization; Approximate optimization (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-52119-6_13

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DOI: 10.1007/978-3-030-52119-6_13

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