TIEA: A tradeoff-based interactive multiobjective evolutionary algorithm

Lu Chen, Bin Xin*, Jie Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes a tradeoff based interactive multiobjective evolutionary algorithm (TIEA). It can support the decision maker (DM) to guide the search towards the DM's most preferred solution by combing evolutionary algorithms with the framework of gradient projection. At each iteration, an evolutionary algorithm is used to generate an approximate Pareto optimal solution. Then, the DM is asked to provide preferences in terms of indifference tradeoffs, the projection of which onto the tangent hyperplane of the Pareto front provides a tradeoff direction. To calculate the projection, an approach to approximate the normal vector of the tangent hyperplane of the Pareto front is developed. Results on nine numerical examples by using TIEA show that TIEA is able to solve multiobjective optimization problems with more than three objectives. Moreover, it can find the Pareto optimal solution that the DM prefers most.

Original languageEnglish
Title of host publicationISCIIA 2016 - 7th International Symposium on Computational Intelligence and Industrial Applications
PublisherFuji Technology Press
ISBN (Electronic)9784990534349
Publication statusPublished - 2016
Event7th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2016 - Beijing, China
Duration: 3 Nov 20166 Nov 2016

Publication series

NameISCIIA 2016 - 7th International Symposium on Computational Intelligence and Industrial Applications

Conference

Conference7th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2016
Country/TerritoryChina
CityBeijing
Period3/11/166/11/16

Keywords

  • Evolutionary algorithm
  • Interactive multiobjective optimization
  • Normal vector approximation
  • Tradeoffs

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