QOBL-SAO 及其变体：用于优化光伏/风能/电池系统和 CEC2020 实际问题的开源软件

IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Software Impacts Pub Date : 2024-02-23 DOI:10.1016/j.simpa.2024.100630

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

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引用次数: 0

摘要

准对立嗅觉代理优化（QOBL-SAO）及其征收飞行变体（LFQOBL-SAO）是用于优化光伏/风能/电池发电系统的两个尖端软件工具。它们还可用于解决现实世界中的 CEC2020 优化问题，与 IUDE、ϵ MAgES 和 iLSHAD ɛ 等性能一流的软件不相上下。QOBL-SAO 利用了随机模式的弱点，然后在初始种群中加入一个数字。而 LFQOBL-SAO 则改进了随机模式的弱点，从而解决了这一问题。LFQOBL-SAO 利用利维飞行代替随机码，从而提高了性能和搜索空间。

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

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, $ϵ$ 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.

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来源期刊

Software Impacts Software

CiteScore

2.70

自引率

9.50%

发文量

审稿时长

16 days