Novel Prediction Strategies for Dynamic Multiobjective Optimization

Qingyang Zhang, Shengxiang Yang*, Shouyong Jiang, Ronggui Wang, Xiaoli Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

This paper proposes a new prediction-based dynamic multiobjective optimization (PBDMO) method, which combines a new prediction-based reaction mechanism and a popular regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) for solving dynamic multiobjective optimization problems. Whenever a change is detected, PBDMO reacts effectively to it by generating three subpopulations based on different strategies. The first subpopulation is created by moving nondominated individuals using a simple linear prediction model with different step sizes. The second subpopulation consists of some individuals generated by a novel sampling strategy to improve population convergence as well as distribution. The third subpopulation comprises some individuals generated using a shrinking strategy based on the probability distribution of variables. These subpopulations are tailored to form a population for the new environment. The experimental results carried out on a variety of bi- and three-objective benchmark functions demonstrate that the proposed technique has competitive performance compared with some state-of-the-art algorithms.

Original languageEnglish
Pages (from-to)260-274
Number of pages15
JournalIEEE Transactions on Evolutionary Computation
Volume24
Issue number2
Early online date13 Jun 2019
DOIs
Publication statusPublished - Apr 2020

Keywords

  • Dynamic multiobjective optimization
  • Nondominated sorting
  • Prediction-based reaction
  • Probability distribution

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