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

{"title":"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","authors":"Abdullahi Abubakar Mas’ud , Ahmed T. Salawudeen , Abubakar A. Umar , Yusuf A. Shaaban , Firdaus Muhammad-Sukki , Umar Musa , Saud J. Alshammari","doi":"10.1016/j.simpa.2024.100630","DOIUrl":null,"url":null,"abstract":"<div><p>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, <span><math><mi>ϵ</mi></math></span> MAgES and the iLSHAD <span><math><mi>ɛ</mi></math></span>. 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.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"19 ","pages":"Article 100630"},"PeriodicalIF":1.3000,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000186/pdfft?md5=34c0be540ac407437f5949fd3034bd29&pid=1-s2.0-S2665963824000186-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A QOBL-SAO and its variant: An open source software for optimizing PV/wind/battery system and CEC2020 real world problems\",\"authors\":\"Abdullahi Abubakar Mas’ud , Ahmed T. Salawudeen , Abubakar A. Umar , Yusuf A. Shaaban , Firdaus Muhammad-Sukki , Umar Musa , Saud J. Alshammari\",\"doi\":\"10.1016/j.simpa.2024.100630\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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, <span><math><mi>ϵ</mi></math></span> MAgES and the iLSHAD <span><math><mi>ɛ</mi></math></span>. 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.</p></div>\",\"PeriodicalId\":29771,\"journal\":{\"name\":\"Software Impacts\",\"volume\":\"19 \",\"pages\":\"Article 100630\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-02-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2665963824000186/pdfft?md5=34c0be540ac407437f5949fd3034bd29&pid=1-s2.0-S2665963824000186-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Software Impacts\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2665963824000186\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software Impacts","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2665963824000186","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}

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。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Software Impacts Software

CiteScore

2.70

自引率

9.50%

发文量

审稿时长

16 days