Quantum-enhanced multiobjective large-scale optimization via parallelism

Traditional quantum-based evolutionary algorithms are intended to solve single-objective optimization problems or multiobjective small-scale optimization problems. However, multiobjective large-scale optimization problems are continuously emerging in the big-data era. Therefore, the research in this...

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Veröffentlicht in:Swarm and evolutionary computation Jg. 57; S. 100697
Hauptverfasser: Cao, Bin, Fan, Shanshan, Zhao, Jianwei, Yang, Po, Muhammad, Khan, Tanveer, Mohammad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.09.2020
Schlagworte:
Large-scale optimization
Multiobjective large-scale optimization
Quantum mechanics
Quantum-inspired evolutionary algorithm (QIEA)
Multiobjective large-scale optimization
Large-scale optimization
Quantum mechanics
Quantum-inspired evolutionary algorithm (QIEA)
ISSN:2210-6502
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Zusammenfassung:Traditional quantum-based evolutionary algorithms are intended to solve single-objective optimization problems or multiobjective small-scale optimization problems. However, multiobjective large-scale optimization problems are continuously emerging in the big-data era. Therefore, the research in this paper, which focuses on combining quantum mechanics with multiobjective large-scale optimization algorithms, will be beneficial to the study of quantum-based evolutionary algorithms. In traditional quantum-behaved particle swarm optimization (QPSO), particle position uncertainty prevents the algorithm from easily falling into local optima. Inspired by the uncertainty principle of position, the authors propose quantum-enhanced multiobjective large-scale algorithms, which are parallel multiobjective large-scale evolutionary algorithms (PMLEAs). Specifically, PMLEA-QDE, PMLEA-QjDE and PMLEA-QJADE are proposed by introducing the search mechanism of the individual particle from QPSO into differential evolution (DE), differential evolution with self-adapting control parameters (jDE) and adaptive differential evolution with optional external archive (JADE). Moreover, the proposed algorithms are implemented with parallelism to improve the optimization efficiency. Verifications performed on several test suites indicate that the proposed quantum-enhanced algorithms are superior to the state-of-the-art algorithms in terms of both effectiveness and efficiency. •The quantum-based position update strategy in QPSO is integrated into the DE operator of DPCCMOLSEA framework.•The novel variants PMLEA-QjDE and PMLEA-QJADE are presented, in which the quantum parameters are adaptive.•The integration of parallel operation based on MPI substantially reduces the runtime of the quantum-enhanced algorithm.
ISSN:2210-6502
DOI:10.1016/j.swevo.2020.100697