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A novel link-based Multi-objective Grey Wolf Optimizer for Appliances Energy Scheduling Problem

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Abstract

In this paper, a modified version of the Multi-objective Grey Wolf Optimizer (MGWO), known as linked-based GWO (LMGWO), is proposed for the Appliances Energy Scheduling Problem (AESP). The proposed LMGWO is utilized by combining the MGWO searching mechanism with a novel strategy, called neighbourhood selection strategy, to improve local exploitation capabilities. AESP is a problem that can be tackled by searching for the best appliances schedule according to a set of constraints and a dynamic pricing scheme(s) utilized for optimizing energy consumed at a particular period. Three objectives are considered to handle AESP: improving user comfort while reducing electricity bills and maintaining power systems’ performance. Therefore, AESP is modelled as a multi-objective optimization problem to handle all objectives simultaneously. In the evaluation results, the LMGWO is tested using a new dataset containing 30 power consumption scenarios with up to 36 appliances. For comparative purposes, the same linked-based neighbourhood selection strategy is utilized with other three optimization algorithms, including particle swarm optimization, salp swarm optimization, and wind-driven algorithm. The performance of the modified versions is compared with each other and that of the original versions to show their improvements. Furthermore, the proposed LMGWO is compared with eight state-of-the-art methods using their recommended datasets to show the viability of the proposed LMGWO. Interestingly, the proposed LMGWO is able to outperform the compared methods in almost all produced results.

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Data Availibility Statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by Ajman University [grant numbers 2021-IRG-ENIT-6]

Author information

Authors and Affiliations

  1. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates

    Sharif Naser Makhadmeh & Mohammed Azmi Al-Betar

  2. Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates

    Ammar Kamal Abasi

  3. ECE Department-Faculty of Engineering, University of Kufa, P.O. Box 21, Najaf, Iraq

    Zaid Abdi Alkareem Alyasseri

  4. Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, P.O. Box 50, Al-Huson, Irbid, Jordan

    Mohammed Azmi Al-Betar

  5. Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine

    Mohammed A. Awadallah

  6. MLALP Research Group, University of Sharjah, Sharjah, United Arab Emirates

    Osama Ahmad Alomari

  7. Computing Department, American University of Kuwait, Salmiya, Kuwait

    Iyad Abu Doush

  8. Computer Science Department, Yarmouk University, Irbid, Jordan

    Iyad Abu Doush

  9. Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates

    Mohammed A. Awadallah

  10. College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq

    Zaid Abdi Alkareem Alyasseri

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  1. Sharif Naser Makhadmeh
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  2. Ammar Kamal Abasi
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  3. Mohammed Azmi Al-Betar
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  4. Mohammed A. Awadallah
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  5. Iyad Abu Doush
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Contributions

Sharif Naser Makhadmeh: Conceptualization, Methodology, Software, Formal analysis, Investigation, Programming, Writing the original draft, Review and editing. Ammar Kamal Abasi: Formal analysis, Investigation, Programming, Methodology, Writing the original draft. Mohammed Azmi Al-betar: Methodology, Writing the original draft, Editing. Mohammed A. Awadallah: Writing the original draft, Review and Editing. Iyad Abu Doush: Writing the original draft, Review and Editing. Zaid Abdi Alkareem Alyasseri: Statistical test, Writing the original draft. Osama Ahmad Alomari: Formal analysis, Investigation, Editing.

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Correspondence to Sharif Naser Makhadmeh.

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Makhadmeh, S.N., Abasi, A.K., Al-Betar, M.A. et al. A novel link-based Multi-objective Grey Wolf Optimizer for Appliances Energy Scheduling Problem. Cluster Comput 25, 4355–4382 (2022). https://doi.org/10.1007/s10586-022-03675-3

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