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

作     者：Makhadmeh, Sharif Naser Abasi, Ammar Kamal Al-Betar, Mohammed Azmi Awadallah, Mohammed A. Abu Doush, Iyad Alyasseri, Zaid Abdi Alkareem Alomari, Osama Ahmad 

作者机构：Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Mohamed Bin Zayed Univ Artificial Intelligence MB Machine Learning Dept Abu Dhabi U Arab Emirates Univ Kufa Fac Engn ECE Dept POB 21 Najaf Iraq Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol POB 50 Irbid Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine Univ Sharjah MLALP Res Grp Sharjah U Arab Emirates Amer Univ Kuwait Comp Dept Salmiya Kuwait Yarmouk Univ Comp Sci Dept Irbid Jordan Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Univ Warith Al Anbiyaa Coll Engn Karbala Iraq 

出 版 物：《CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS》 (簇计算)

年 卷 期：2022年第25卷第6期

页      面：4355-4382页

核心收录：

学科分类：07[理学] 0703[理学-化学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Ajman University [2021-IRG-ENIT-6] 

主　　题：Multi-objective Grey Wolf Optimizer Neighbourhood Selection Strategy Linked Based Grey Wolf Optimizer Appliances Energy Scheduling Problem 

摘      要：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.