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 and Bilal Ahmad
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Qi You: Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Wuxi 214122, China
Jun Sun: Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Wuxi 214122, China
Feng Pan: Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Wuxi 214122, China
Vasile Palade: Faculty of Engineering and Computing, Coventry University, Coventry CV1 5FB, UK
Bilal Ahmad: Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Wuxi 214122, China

Mathematics, 2021, vol. 9, issue 16, 1-20

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.

Keywords: decomposition; multi-objective optimization; quantum-behaved particle swarm optimization; premature convergence; diversity control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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