A Mixture-of-Experts Prediction Framework for Evolutionary Dynamic Multiobjective Optimization

Kay Chen Tan, Min Jiang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

Dynamic multiobjective optimization requires the robust tracking of varying Pareto-optimal solutions (POS) in a changing environment. When a change is detected in the environment, prediction mechanisms estimate the POS by utilizing information from previous populations to accelerate search toward the true POS. To achieve a robust prediction of POS, a mixture-of-experts-based ensemble framework is proposed. Unlike existing approaches, the framework utilizes multiple prediction mechanisms to improve the overall prediction. A gating network is applied to manage switching among the various predictors based on performance of the predictors at different time intervals of the optimization process. The efficacy of the proposed framework is validated through experimental studies based on 13 dynamic multiobjective benchmark optimization problems. The simulation results show that the proposed framework improves the dynamic optimization performance significantly, particularly for: 1) problems with distinct dynamic POS in decision space over time and 2) problems with highly nonlinear decision variable linkages.

Original languageEnglish
Article number8698315
Pages (from-to)5099-5112
Number of pages14
JournalIEEE Transactions on Cybernetics
Volume50
Issue number12
DOIs
Publication statusPublished - Dec 2020
Externally publishedYes

Keywords

  • Dynamic multiobjective optimization
  • evolutionary algorithms (EAs)
  • mixture-of-experts (MoE)

ASJC Scopus subject areas

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

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