QPSO-CD: quantum-behaved particle swarm optimization algorithm with Cauchy distribution

Motivated by the particle swarm optimization (PSO) and quantum computing theory, we have presented a quantum variant of PSO (QPSO) mutated with Cauchy operator and natural selection mechanism (QPSO-CD) from evolutionary computations. The performance of proposed hybrid quantum-behaved particle swarm...

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Veröffentlicht in:Quantum information processing Jg. 19; H. 10
Hauptverfasser: Bhatia, Amandeep Singh, Saggi, Mandeep Kaur, Zheng, Shenggen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.10.2020
Springer Nature B.V
Schlagworte:
Algorithms
Data Structures and Information Theory
Mathematical Physics
Optimization
Particle swarm optimization
Physics
Physics and Astronomy
Quantum Computing
Quantum Information Technology
Quantum Physics
Spintronics
Quantum-behaved algorithm
Quantum computing
Engineering design problems
Particle swarm optimization
Cauchy distribution
ISSN:1570-0755, 1573-1332
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Zusammenfassung:Motivated by the particle swarm optimization (PSO) and quantum computing theory, we have presented a quantum variant of PSO (QPSO) mutated with Cauchy operator and natural selection mechanism (QPSO-CD) from evolutionary computations. The performance of proposed hybrid quantum-behaved particle swarm optimization with Cauchy distribution (QPSO-CD) is investigated and compared with its counterparts based on a set of benchmark problems. Moreover, QPSO-CD is employed in well-studied constrained engineering problems to investigate its applicability. Further, the correctness and time complexity of QPSO-CD are analyzed and compared with the classical PSO. It has been proved that QPSO-CD handles such real-life problems efficiently and can attain superior solutions in most of the problems. The experimental results shown that QPSO associated with Cauchy distribution and natural selection strategy outperforms other variants in context of stability and convergence.
Bibliographie:ObjectType-Article-1
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ISSN:1570-0755
1573-1332
DOI:10.1007/s11128-020-02842-y