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An Adaptive Particle Swarm Optimization Algorithm Based on Directed Weighted Complex Network

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  • Ming Li
  • Wenqiang Du
  • Fuzhong Nian

Abstract

The disadvantages of particle swarm optimization (PSO) algorithm are that it is easy to fall into local optimum in high-dimensional space and has a low convergence rate in the iterative process. To deal with these problems, an adaptive particle swarm optimization algorithm based on directed weighted complex network (DWCNPSO) is proposed. Particles can be scattered uniformly over the search space by using the topology of small-world network to initialize the particles position. At the same time, an evolutionary mechanism of the directed dynamic network is employed to make the particles evolve into the scale-free network when the in-degree obeys power-law distribution. In the proposed method, not only the diversity of the algorithm was improved, but also particles’ falling into local optimum was avoided. The simulation results indicate that the proposed algorithm can effectively avoid the premature convergence problem. Compared with other algorithms, the convergence rate is faster.

Suggested Citation

  • Ming Li & Wenqiang Du & Fuzhong Nian, 2014. "An Adaptive Particle Swarm Optimization Algorithm Based on Directed Weighted Complex Network," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-7, April.
    • Handle: RePEc:hin:jnlmpe:434972
    DOI: 10.1155/2014/434972
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    Cited by:

    1. Alabi, Tobi Michael & Lawrence, Nathan P. & Lu, Lin & Yang, Zaiyue & Bhushan Gopaluni, R., 2023. "Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system," Applied Energy, Elsevier, vol. 333(C).
    2. Hiramani Shukla & Srete Nikolovski & More Raju & Ankur Singh Rana & Pawan Kumar, 2022. "SMES-GCSC Coordination for Frequency and Voltage Regulation in a Multi-Area and Multi-Source Power System with Penetration of Electric Vehicles and Renewable Energy Sources," Energies, MDPI, vol. 16(1), pages 1-27, December.
    3. Yifei Chen & Zhihan Fu, 2023. "Multi-Step Ahead Forecasting of the Energy Consumed by the Residential and Commercial Sectors in the United States Based on a Hybrid CNN-BiLSTM Model," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    4. Jain, Sonal & Ramesh, Dharavath & Trivedi, Munesh C. & Edla, Damodar Reddy, 2023. "Evaluation of metaheuristic optimization algorithms for optimal allocation of surface water and groundwater resources for crop production," Agricultural Water Management, Elsevier, vol. 279(C).

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