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A Distributed Quantum-Behaved Particle Swarm Optimization Using Opposition-Based Learning on Spark for Large-Scale Optimization Problem

Author

Listed:
  • Zhaojuan Zhang

    (College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China)

  • Wanliang Wang

    (College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China)

  • Gaofeng Pan

    (Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA)

Abstract

In the era of big data, the size and complexity of the data are increasing especially for those stored in remote locations, and whose difficulty is further increased by the ongoing rapid accumulation of data scale. Real-world optimization problems present new challenges to traditional intelligent optimization algorithms since the traditional serial optimization algorithm has a high computational cost or even cannot deal with it when faced with large-scale distributed data. Responding to these challenges, a distributed cooperative evolutionary algorithm framework using Spark (SDCEA) is first proposed. The SDCEA can be applied to address the challenge due to insufficient computing resources. Second, a distributed quantum-behaved particle swarm optimization algorithm (SDQPSO) based on the SDCEA is proposed, where the opposition-based learning scheme is incorporated to initialize the population, and a parallel search is conducted on distributed spaces. Finally, the performance of the proposed SDQPSO is tested. In comparison with SPSO, SCLPSO, and SALCPSO, SDQPSO can not only improve the search efficiency but also search for a better optimum with almost the same computational cost for the large-scale distributed optimization problem. In conclusion, the proposed SDQPSO based on the SDCEA framework has high scalability, which can be applied to solve the large-scale optimization problem.

Suggested Citation

  • Zhaojuan Zhang & Wanliang Wang & Gaofeng Pan, 2020. "A Distributed Quantum-Behaved Particle Swarm Optimization Using Opposition-Based Learning on Spark for Large-Scale Optimization Problem," Mathematics, MDPI, vol. 8(11), pages 1-21, October.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:1860-:d:433669
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    References listed on IDEAS

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    1. Humberto Verdejo & Victor Pino & Wolfgang Kliemann & Cristhian Becker & José Delpiano, 2020. "Implementation of Particle Swarm Optimization (PSO) Algorithm for Tuning of Power System Stabilizers in Multimachine Electric Power Systems," Energies, MDPI, vol. 13(8), pages 1-29, April.
    2. Ghasemi, Peiman & Khalili-Damghani, Kaveh & Hafezalkotob, Ashkan & Raissi, Sadigh, 2019. "Uncertain multi-objective multi-commodity multi-period multi-vehicle location-allocation model for earthquake evacuation planning," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 105-132.
    3. Yangyang Li & Zhenghan Chen & Yang Wang & Licheng Jiao & Yu Xue, 2017. "A Novel Distributed Quantum-Behaved Particle Swarm Optimization," Journal of Optimization, Hindawi, vol. 2017, pages 1-9, May.
    4. Gülnur Yildizdan & Ömer Kaan Baykan, 2020. "A New Hybrid BA_ABC Algorithm for Global Optimization Problems," Mathematics, MDPI, vol. 8(10), pages 1-36, October.
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    1. Yunshan Lü & Hailing Xiong & Hao Zhou & Xin Guan, 2022. "A Distributed Optimization Accelerated Algorithm with Uncoordinated Time-Varying Step-Sizes in an Undirected Network," Mathematics, MDPI, vol. 10(3), pages 1-17, January.

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