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A Compact Pigeon-Inspired Optimization for Maximum Short-Term Generation Mode in Cascade Hydroelectric Power Station

Author

Listed:
  • Ai-Qing Tian

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Shu-Chuan Chu

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
    College of Science and Engineering, Flinders University, 1284 South Road, Clovelly Park, SA 5042, Australia)

  • Jeng-Shyang Pan

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Huanqing Cui

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Wei-Min Zheng

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

Abstract

Pigeon-inspired optimization (PIO) is a new type of intelligent algorithm. It is proposed that the algorithm simulates the movement of pigeons going home. In this paper, a new pigeon herding algorithm called compact pigeon-inspired optimization (CPIO) is proposed. The challenging task for multiple algorithms is not only combining operations, but also constraining existing devices. The proposed algorithm aims to solve complex scientific and industrial problems with many data packets, including the use of classical optimization problems and the ability to find optimal solutions in many solution spaces with limited hardware resources. A real-valued prototype vector performs probability and statistical calculations, and then generates optimal candidate solutions for CPIO optimization algorithms. The CPIO algorithm was used to evaluate a variety of continuous multi-model functions and the largest model of hydropower short-term generation. The experimental results show that the proposed algorithm is a more effective way to produce competitive results in the case of limited memory devices.

Suggested Citation

  • Ai-Qing Tian & Shu-Chuan Chu & Jeng-Shyang Pan & Huanqing Cui & Wei-Min Zheng, 2020. "A Compact Pigeon-Inspired Optimization for Maximum Short-Term Generation Mode in Cascade Hydroelectric Power Station," Sustainability, MDPI, vol. 12(3), pages 1-19, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:3:p:767-:d:311269
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    References listed on IDEAS

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    Cited by:

    1. Tian, Ai-Qing & Wang, Xiao-Yang & Xu, Heying & Pan, Jeng-Shyang & Snášel, Václav & Lv, Hong-Xia, 2024. "Multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improvement," Energy, Elsevier, vol. 294(C).
    2. Jeng-Shyang Pan & Pei-Cheng Song & Shu-Chuan Chu & Yan-Jun Peng, 2020. "Improved Compact Cuckoo Search Algorithm Applied to Location of Drone Logistics Hub," Mathematics, MDPI, vol. 8(3), pages 1-19, March.
    3. Pan, Jeng-Shyang & Tian, Ai-Qing & Snášel, Václav & Kong, Lingping & Chu, Shu-Chuan, 2022. "Maximum power point tracking and parameter estimation for multiple-photovoltaic arrays based on enhanced pigeon-inspired optimization with Taguchi method," Energy, Elsevier, vol. 251(C).
    4. Jeng-Shyang Pan & Qing-yong Yang & Shu-Chuan Chu & Kuo-Chi Chang, 2021. "Compact Sine Cosine Algorithm applied in vehicle routing problem with time window," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 78(4), pages 609-628, December.
    5. Jing Liu & Yulong Qiao, 2020. "Mahalanobis distance–based kernel supervised machine learning in spectral dimensionality reduction for hyperspectral imaging remote sensing," International Journal of Distributed Sensor Networks, , vol. 16(11), pages 15501477209, November.
    6. Siqi Zhang & Fang Fan & Wei Li & Shu-Chuan Chu & Jeng-Shyang Pan, 2021. "A parallel compact sine cosine algorithm for TDOA localization of wireless sensor network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 78(2), pages 213-223, October.

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