A Quasi oppositional smell agent optimization and its levy flight variant: A PV/Wind/battery system optimization application

In this study, two novel algorithms are developed: the quasi-oppositional smell agent optimization (QOBL-SAO) and its levy flight variation (LFQOBL-SAO), and their performance is compared to that of the conventional smell agent optimization (SAO). Two investigations were carried out. First, the capa...

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Published in:APPLIED SOFT COMPUTING
Main Authors: Mas'ud, Abdullahi Abubakar; Salawudeen, Ahmed T.; Umar, Abubakar Ahmad; Aziz, Ali Saleh; Shaaban, Yusuf A.; Muhammad-Sukki, Firdaus; Musa, Umar
Format: Article
Language:English
Published: ELSEVIER 2023
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001149591700001
author Mas'ud
Abdullahi Abubakar; Salawudeen
Ahmed T.; Umar
Abubakar Ahmad; Aziz
Ali Saleh; Shaaban
Yusuf A.; Muhammad-Sukki
Firdaus; Musa
Umar
spellingShingle Mas'ud
Abdullahi Abubakar; Salawudeen
Ahmed T.; Umar
Abubakar Ahmad; Aziz
Ali Saleh; Shaaban
Yusuf A.; Muhammad-Sukki
Firdaus; Musa
Umar
A Quasi oppositional smell agent optimization and its levy flight variant: A PV/Wind/battery system optimization application
Computer Science
author_facet Mas'ud
Abdullahi Abubakar; Salawudeen
Ahmed T.; Umar
Abubakar Ahmad; Aziz
Ali Saleh; Shaaban
Yusuf A.; Muhammad-Sukki
Firdaus; Musa
Umar
author_sort Mas'ud
spelling Mas'ud, Abdullahi Abubakar; Salawudeen, Ahmed T.; Umar, Abubakar Ahmad; Aziz, Ali Saleh; Shaaban, Yusuf A.; Muhammad-Sukki, Firdaus; Musa, Umar
A Quasi oppositional smell agent optimization and its levy flight variant: A PV/Wind/battery system optimization application
APPLIED SOFT COMPUTING
English
Article
In this study, two novel algorithms are developed: the quasi-oppositional smell agent optimization (QOBL-SAO) and its levy flight variation (LFQOBL-SAO), and their performance is compared to that of the conventional smell agent optimization (SAO). Two investigations were carried out. First, the capabilities of the novel algorithms were tested in solving ten benchmarked functions and five CEC2020 real-world optimization problems. Second, they are applied to optimize the hybrid photovoltaic (PV)/wind/battery, PV/battery, and wind/battery power system for a healthcare center in a Nigerian village. Load demand, PV and wind profiles of the aforementioned location were used to develop the hybrid system. All simulations were carried out in MATLAB software. The results show that the novel algorithms can solve benchmarked functions and the CEC2020 real-world constrained optimization competition. In particular, the performance of the QOBL and the LF-QOBL are as good as the top performing functions like the IUDE, epsilon MAgES and the iLSHAD epsilon algorithms. However, in terms of convergence time, lowest cost of energy (LCE), and total annualized cost (TAC), the novel algorithms outperformed the SAO for the PV/wind/battery optimization application. The hybrid PV/wind/battery system is the most cost-effective when using LFQOBL-SAO and QOBL-SAO, with a TAC value of $15100. Furthermore, the results demonstrate that the LFQOBL-SAO method is accurate and outperforms the QOBL-SAO and SAO algorithms.(c) 2023 Elsevier B.V. All rights reserved.
ELSEVIER
1568-4946
1872-9681
2023
147

10.1016/j.asoc.2023.110813
Computer Science

WOS:001149591700001
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001149591700001
title A Quasi oppositional smell agent optimization and its levy flight variant: A PV/Wind/battery system optimization application
title_short A Quasi oppositional smell agent optimization and its levy flight variant: A PV/Wind/battery system optimization application
title_full A Quasi oppositional smell agent optimization and its levy flight variant: A PV/Wind/battery system optimization application
title_fullStr A Quasi oppositional smell agent optimization and its levy flight variant: A PV/Wind/battery system optimization application
title_full_unstemmed A Quasi oppositional smell agent optimization and its levy flight variant: A PV/Wind/battery system optimization application
title_sort A Quasi oppositional smell agent optimization and its levy flight variant: A PV/Wind/battery system optimization application
container_title APPLIED SOFT COMPUTING
language English
format Article
description In this study, two novel algorithms are developed: the quasi-oppositional smell agent optimization (QOBL-SAO) and its levy flight variation (LFQOBL-SAO), and their performance is compared to that of the conventional smell agent optimization (SAO). Two investigations were carried out. First, the capabilities of the novel algorithms were tested in solving ten benchmarked functions and five CEC2020 real-world optimization problems. Second, they are applied to optimize the hybrid photovoltaic (PV)/wind/battery, PV/battery, and wind/battery power system for a healthcare center in a Nigerian village. Load demand, PV and wind profiles of the aforementioned location were used to develop the hybrid system. All simulations were carried out in MATLAB software. The results show that the novel algorithms can solve benchmarked functions and the CEC2020 real-world constrained optimization competition. In particular, the performance of the QOBL and the LF-QOBL are as good as the top performing functions like the IUDE, epsilon MAgES and the iLSHAD epsilon algorithms. However, in terms of convergence time, lowest cost of energy (LCE), and total annualized cost (TAC), the novel algorithms outperformed the SAO for the PV/wind/battery optimization application. The hybrid PV/wind/battery system is the most cost-effective when using LFQOBL-SAO and QOBL-SAO, with a TAC value of $15100. Furthermore, the results demonstrate that the LFQOBL-SAO method is accurate and outperforms the QOBL-SAO and SAO algorithms.(c) 2023 Elsevier B.V. All rights reserved.
publisher ELSEVIER
issn 1568-4946
1872-9681
publishDate 2023
container_volume 147
container_issue
doi_str_mv 10.1016/j.asoc.2023.110813
topic Computer Science
topic_facet Computer Science
accesstype
id WOS:001149591700001
url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001149591700001
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