A Multipopulation-Based Multiobjective Evolutionary Algorithm

Haiping Ma, Minrui Fei, Zheheng Jiang, Ling Li, Huiyu Zhou, Danny Crookes

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Multipopulation is an effective optimization component often embedded into evolutionary algorithms to solve optimization problems. In this paper, a new multipopulation-based multiobjective genetic algorithm (MOGA) is proposed, which uses a unique cross-subpopulation migration process inspired by biological processes to share information between subpopulations. Then, a Markov model of the proposed multipopulation MOGA is derived, the first of its kind, which provides an exact mathematical model for each possible population occurring simultaneously with multiple objectives. Simulation results of two multiobjective test problems with multiple subpopulations justify the derived Markov model, and show that the proposed multipopulation method can improve the optimization ability of the MOGA. Also, the proposed multipopulation method is applied to other multiobjective evolutionary algorithms (MOEAs) for evaluating its performance against the IEEE Congress on Evolutionary Computation multiobjective benchmarks. The experimental results show that a single-population MOEA can be extended to a multipopulation version, while obtaining better optimization performance.

Original languageEnglish
JournalIEEE Transactions on Cybernetics
Early online date05 Oct 2018
Publication statusEarly online date - 05 Oct 2018

Bibliographical note

Publisher Copyright:

Copyright 2018 Elsevier B.V., All rights reserved.


  • Evolutionary algorithm
  • Evolutionary computation
  • Genetic algorithms
  • genetic algorithms (GAs)
  • Markov chain
  • Markov processes
  • Mathematical model
  • multiobjective optimization
  • multipopulation
  • Optimization
  • Sociology
  • Statistics

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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