Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine

Weizhen Hu, Min Jiang*, Xing Gao, Kay Chen Tan, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

The main feature of the Dynamic Multi-objective Optimization Problems (DMOPs) is that optimization objective functions will change with times or environments. One of the promising approaches for solving the DMOPs is reusing the obtained Pareto optimal set (POS) to train prediction models via machine learning approaches. In this paper, we train an Incremental Support Vector Machine (ISVM) classifier with the past POS, and then the solutions of the DMOP we want to solve at the next moment are filtered through the trained ISVM classifier. A high-quality initial population will be generated by the ISVM classifier, and a variety of different types of population-based dynamic multi-objective optimization algorithms can benefit from the population. To verify this idea, we incorporate the proposed approach into three evolutionary algorithms, the multi-objective particle swarm optimization(MOPSO), Nondominated Sorting Genetic Algorithm II (NSGA-II), and the Regularity Model-based multi-objective estimation of distribution algorithm(RE-MEDA). We employ experimentS to test these algorithms, and experimental results show the effectiveness.

Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2794-2799
Number of pages6
ISBN (Electronic)9781728121536
DOIs
Publication statusPublished - Jun 2019
Event2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand
Duration: 10 Jun 201913 Jun 2019

Publication series

Name2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Conference

Conference2019 IEEE Congress on Evolutionary Computation, CEC 2019
Country/TerritoryNew Zealand
CityWellington
Period10/06/1913/06/19

Scopus Subject Areas

  • Computational Mathematics
  • Modelling and Simulation

User-Defined Keywords

  • Dynamic Multi-objective Optimization Problems
  • Incremental Support Vector Machine
  • Pareto Optimal Set

Fingerprint

Dive into the research topics of 'Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine'. Together they form a unique fingerprint.

Cite this