A Modified Coronavirus Herd Immunity Optimizer for the Power Scheduling Problem

Mathematics

Sharif Naser Makhadmeh¹  Mohammed Azmi Al-Betar¹  Mazin Abed Mohammed²  Iyad Abu Doush³  Audrius Zajančkauskas⁴  Robertas Damaševičius⁴  Mohammed A. Awadallah⁵  Zaid Abdi Alkareem Alyasseri⁶  Osama Ahmad Alomari⁷  Ammar Kamal Abasi⁸ 

[1] Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates;College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq;Computing Department, College of Engineering and Applied Sciences, American University of Kuwait, Salmiya 20002, Kuwait;Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania;Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza P860, Palestine;Information Technology Research and Development Center (ITRDC), University of Kufa, Kufa 54001, Iraq;MLALP Research Group, University of Sharjah, Sharjah 346, United Arab Emirates;School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia;

关键词: discrete coronavirus herd immunity optimizer;    power scheduling problem in smart home;    multi-criteria optimisation;    smart home;    multi-objective optimisation problem;   

DOI  :  10.3390/math10030315

来源: DOAJ

【 摘 要 】

The Coronavirus herd immunity optimizer (CHIO) is a new human-based optimization algorithm that imitates the herd immunity strategy to eliminate of the COVID-19 disease. In this paper, the coronavirus herd immunity optimizer (CHIO) is modified to tackle a discrete power scheduling problem in a smart home (PSPSH). PSPSH is a combinatorial optimization problem with NP-hard features. It is a highly constrained discrete scheduling problem concerned with assigning the operation time for smart home appliances based on a dynamic pricing scheme(s) and several other constraints. The primary objective when solving PSPSH is to maintain the stability of the power system by reducing the ratio between average and highest power demand (peak-to-average ratio (PAR)) and reducing electricity bill (EB) with considering the comfort level of users (UC). This paper modifies and adapts the CHIO algorithm to deal with such discrete optimization problems, particularly PSPSH. The adaptation and modification include embedding PSPSH problem-specific operators to CHIO operations to meet the discrete search space requirements. PSPSH is modeled as a multi-objective problem considering all objectives, including PAR, EB, and UC. The proposed method is examined using a dataset that contains 36 home appliances and seven consumption scenarios. The main CHIO parameters are tuned to find their best values. These best values are used to evaluate the proposed method by comparing its results with comparative five metaheuristic algorithms. The proposed method shows encouraging results and almost obtains the best results in all consumption scenarios.

【 授权许可】

Unknown