ICB-MOEA/D: An interactive classification-based multi-objective optimization algorithm

  • Bin Xin*
  • , Hepeng Li
  • , Ling Wang
  • *Corresponding author for this work

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

3 Citations (Scopus)

Abstract

Interactive multi-objective optimization algorithms have developed rapidly in recent years. In this paper, we propose a new classification-based interactive multi-objective optimization algorithm named ICB-MOEA/D to solve the formulated multiobjective optimization problem. ICB-MOEA/D provides several solutions for the decision maker to choose. The decision maker chooses his/her most preferred solution from these solutions and the historical solutions which have been chosen as the current most preferred solution. ICB-MOEA/D records this solution and classifies the objectives according to the updated preference information into four categories: 1) objectives which are expected to be improved; 2) objectives which can be sacrificed; 3) objectives which are expected to remain basically unchanged; 4) objectives which do not matter currently. Accoding to the number of the objectives in the first category, a new single-objective opitimization model or multi -objective optimization model will be built. The single-objective optimization model will be optimized by a classic variant of differential evolution DE/rand/llhin, and the multi-objective optimization model will be optimized by a popular docomposition-based multi-objective optimizer MOEA/D. All the classifications are done automatically by the algorithm, reducing the burden of the decision maker. ICB-MOEAlD was tested on the two-objective instance ZDT1, and the experiment results show the effectiveness of ICB-MOEA/D.

Original languageEnglish
Title of host publicationProceedings of the 37th Chinese Control Conference, CCC 2018
EditorsXin Chen, Qianchuan Zhao
PublisherIEEE Computer Society
Pages2500-2505
Number of pages6
ISBN (Electronic)9789881563941
DOIs
Publication statusPublished - 5 Oct 2018
Event37th Chinese Control Conference, CCC 2018 - Wuhan, China
Duration: 25 Jul 201827 Jul 2018

Publication series

NameChinese Control Conference, CCC
Volume2018-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference37th Chinese Control Conference, CCC 2018
Country/TerritoryChina
CityWuhan
Period25/07/1827/07/18

Keywords

  • Classification
  • Decomposition
  • Interactive multi-objective optimization
  • Virtual decision maker

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