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The Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP) Method for Solving Wireless Sensor Networks Optimization Problems in Smart Grid Applications

Author

Listed:
  • Hisham A. Shehadeh

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Mohd Yamani Idna Idris

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Ismail Ahmedy

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Roziana Ramli

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Noorzaily Mohamed Noor

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

Prior studies in Wireless Sensor Network (WSN) optimization mostly concentrate on maximizing network coverage and minimizing network energy consumption. However, there are other factors that could affect the WSN Quality of Service (QoS). In this paper, four objective functions that affect WSN QoS, namely end-to-end delay, end-to-end latency, network throughput and energy efficiency are studied. Optimal value of packet payload size that is able to minimize the end-to-end delay and end-to-end latency, while also maximizing the network throughput and energy efficiency is sought. To do this, a smart grid application case study together with a WSN QoS model is used to find the optimal value of the packet payload size. Our proposed method, named Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP), along with other three state-of-the-art multi-objective optimization algorithms known as OMOPSO, NSGA-II and SPEA2, are utilized in this study. Different packet payload sizes are supplied to the algorithms and their optimal value is derived. From the experiments, the knee point and the intersection point of all the obtained Pareto fronts for all the algorithms show that the optimal packet payload size that manages the trade-offs between the four objective functions is equal to 45 bytes. The results also show that the performance of our proposed MOSFP method is highly competitive and found to have the best average value compared to the other three algorithms. Furthermore, the overall performance of MOSFP on four objective functions outperformed OMOPSO, NSGA-II and SPEA2 by 3%, 6% and 51%, respectively.

Suggested Citation

  • Hisham A. Shehadeh & Mohd Yamani Idna Idris & Ismail Ahmedy & Roziana Ramli & Noorzaily Mohamed Noor, 2018. "The Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP) Method for Solving Wireless Sensor Networks Optimization Problems in Smart Grid Applications," Energies, MDPI, vol. 11(1), pages 1-35, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:97-:d:125152
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    References listed on IDEAS

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    1. Kabalci, Yasin, 2016. "A survey on smart metering and smart grid communication," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 302-318.
    2. J�r�me Massiani & Gabriele Picco, 2014. "The opportunity costs of public funds concepts and issues," Working Papers 2014:20, Department of Economics, University of Venice "Ca' Foscari".
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    Cited by:

    1. Nandakumar Sundararaju & Arangarajan Vinayagam & Veerapandiyan Veerasamy & Gunasekaran Subramaniam, 2022. "A Chaotic Search-Based Hybrid Optimization Technique for Automatic Load Frequency Control of a Renewable Energy Integrated Power System," Sustainability, MDPI, vol. 14(9), pages 1-27, May.
    2. Obadah Said Solaiman & Rami Sihwail & Hisham Shehadeh & Ishak Hashim & Kamal Alieyan, 2023. "Hybrid Newton–Sperm Swarm Optimization Algorithm for Nonlinear Systems," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    3. Meihua Wang & Wei-Chang Yeh & Ta-Chung Chu & Xianyong Zhang & Chia-Ling Huang & Jun Yang, 2018. "Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm," Energies, MDPI, vol. 11(9), pages 1-23, September.
    4. Zi-Jia Wang & Zhi-Hui Zhan & Jun Zhang, 2018. "Solving the Energy Efficient Coverage Problem in Wireless Sensor Networks: A Distributed Genetic Algorithm Approach with Hierarchical Fitness Evaluation," Energies, MDPI, vol. 11(12), pages 1-14, December.

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