DMO-QPSO: A multi-objective quantum-behaved particle swarm optimization algorithm based on decomposition with diversity control

Qi You, Jun Sun, Feng Pan, Vasile Palade, Bilal Ahmad

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
7 Downloads (Pure)

Abstract

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has shown remarkable effectiveness in solving multi-objective problems (MOPs). In this paper, we integrate the quantum-behaved particle swarm optimization (QPSO) algorithm with the MOEA/D framework in order to make the QPSO be able to solve MOPs effectively, with the advantage of the QPSO being fully used. We also employ a diversity controlling mechanism to avoid the premature convergence especially at the later stage of the search process, and thus further improve the performance of our proposed algorithm. In addition, we introduce a number of nondominated solutions to generate the global best for guiding other particles in the swarm. Experiments are conducted to compare the proposed algorithm, DMO-QPSO, with four multi-objective particle swarm optimization algorithms and one multi-objective evolutionary algorithm on 15 test functions, including both bi-objective and tri-objective problems. The results show that the performance of the proposed DMO-QPSO is better than other five algorithms in solving most of these test problems. Moreover, we further study the impact of two different decomposition approaches, i.e., the penalty-based boundary intersection (PBI) and Tchebycheff (TCH) approaches, as well as the polynomial mutation operator on the algorithmic performance of DMO-QPSO.

Original languageEnglish
Article number1959
Number of pages20
JournalMathematics
Volume9
Issue number16
DOIs
Publication statusPublished - 16 Aug 2021

Bibliographical note

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Keywords

  • Decomposition
  • Diversity control
  • Multi-objective optimization
  • Premature convergence
  • Quantum-behaved particle swarm optimization

ASJC Scopus subject areas

  • Mathematics(all)

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