Surrogate Approximation on Bilevel Multi Follower Optimization Problems

Md Monjurul Islam, Abu Barkat ullah, Md. Hasan Furhad, Saiba Nazah

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

Abstract

Bilevel programming means studying decentralized non-cooperative and cooperative decision systems which contains two levels known as upper (Leader) and lower (Follower) level problems. By nature these types of problems are computationally expensive and have nested characteristics. It is even more complex when multiple follower is involved at the lower level which requires Stackelberg Nash Equilibrium to be satisfied. As these type of problems are involved in the scenario of transportation, logistics and environmental science, it is highly demanding to make efficient algorithm which requires comparatively low cost. However, so far in our knowledge nobody has tried to solve multi follower bilevel problems by using surrogate approximation approach which uses less computational cost than the traditional meta heuristics algorithm. In this paper, we incorporate Design and Analysis of Computer Experiments (DACE) model for solving lower level problems which contributes to the reduction of the computational cost. Our analysis involved memetic bilevel approach with surrogate approximation at the lower level and upper level contains traditional differential evaluation strategy. Our proposed approach shows competitive result with low cost than the existing approaches.
Original languageEnglish
Title of host publicationIEEE Symposium Series on Computational Intelligence (SSCI)
EditorsHussein Abbass, Carlos A. Coello Coello, Hemant Kumar Singh
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)9781728125473
ISBN (Print)9781728125480
DOIs
Publication statusPublished - 5 Jan 2021
Event2020 IEEE Symposium Series on Computational Intelligence (SSCI) - Canberra, Canberra, Australia
Duration: 1 Dec 20204 Dec 2020
http://www.ieeessci2020.org/

Conference

Conference2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Abbreviated titleSSCI 2020
CountryAustralia
CityCanberra
Period1/12/204/12/20
Internet address

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