A QOBL-SAO and its variant: An open source software for optimizing PV/wind/battery system and CEC2020 real world problems

Mas’ud, Abdullahi Abubakar; Salawudeen, Ahmed T.; Umar, Abubakar A.; Shaaban, Yusuf A.; Muhammad-Sukki, Firdaus; Musa, Umar; Alshammari, Saud J.

Abstract

The Quasi oppositional smell agent optimization (QOBL-SAO) and its levy flight variant (LFQOBL-SAO) are two cutting-edge software tools for optimizing PV/wind/battery power systems. They can also be used to solve real-world CEC2020 optimization problems and are as good as top-performing software such as IUDE, MAgES and the iLSHAD ɛ. The QOBL-SAO exploits the random mode’s weakness and then adds a number to the initial population. The LFQOBL-SAO, on the other hand, improves the random mode’s weakness in order to solve this problem. The LFQOBL-SAO improves performance and search space by using levy flight instead of random code.

• Journal Article Type: Article
• Acceptance Date: Feb 17, 2024
• Online Publication Date: Feb 23, 2024
• Publication Date: 2024-03
• Deposit Date: Feb 26, 2024
• Publicly Available Date: Feb 26, 2024
• Journal: Software Impacts
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Article Number: 100630
• DOI: https://doi.org/10.1016/j.simpa.2024.100630
• Keywords: Smell agent optimization; Levy flight smell agent optimization; Quasi oppositional smell agent optimization
• Public URL: http://researchrepository.napier.ac.uk/Output/3524028
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S2665963824000186