Solving Multiobjective Optimization Problems in Unknown Dynamic Environments: An Inverse Modeling Approach

Sen Bong Gee, Kay Chen Tan, Cesare Alippi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

72 Citations (Scopus)

Abstract

Evolutionary multiobjective optimization in dynamic environments is a challenging task, as it requires the optimization algorithm converging to a time-variant Pareto optimal front. This paper proposes a dynamic multiobjective optimization algorithm which utilizes an inverse model set to guide the search toward promising decision regions. In order to reduce the number of fitness evalutions for change detection purpose, a two-stage change detection test is proposed which uses the inverse model set to check potential changes in the objective function landscape. Both static and dynamic multiobjective benchmark optimization problems have been considered to evaluate the performance of the proposed algorithm. Experimental results show that the improvement in optimization performance is achievable when the proposed inverse model set is adopted.

Original languageEnglish
Article number7564454
Pages (from-to)4223-4234
Number of pages12
JournalIEEE Transactions on Cybernetics
Volume47
Issue number12
DOIs
Publication statusPublished - Dec 2017
Externally publishedYes

Keywords

  • Change detection
  • decomposition
  • dynamic multiobjective optimization
  • evolutionary computation

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Solving Multiobjective Optimization Problems in Unknown Dynamic Environments: An Inverse Modeling Approach'. Together they form a unique fingerprint.

Cite this