{"title":"Effective arithmetic optimization algorithm with probabilistic search strategy for function optimization problems","authors":"Lu Peng , Chaohao Sun , Wenli Wu","doi":"10.1016/j.dsm.2022.08.002","DOIUrl":null,"url":null,"abstract":"<div><p>This paper proposes an enhanced arithmetic optimization algorithm (AOA) called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA. Furthermore, an adjustable parameter is also developed to balance the exploration and exploitation operations. In addition, a jump mechanism is included in the PSAOA to assist individuals in jumping out of local optima. Using 29 classical benchmark functions, the proposed PSAOA is extensively tested. Compared to the AOA and other well-known methods, the experiments demonstrated that the proposed PSAOA beats existing comparison algorithms on the majority of the test functions.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764922000315/pdfft?md5=4a4f5d81d0c4eab41e184daef9f1971f&pid=1-s2.0-S2666764922000315-main.pdf","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science and Management","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666764922000315","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
This paper proposes an enhanced arithmetic optimization algorithm (AOA) called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA. Furthermore, an adjustable parameter is also developed to balance the exploration and exploitation operations. In addition, a jump mechanism is included in the PSAOA to assist individuals in jumping out of local optima. Using 29 classical benchmark functions, the proposed PSAOA is extensively tested. Compared to the AOA and other well-known methods, the experiments demonstrated that the proposed PSAOA beats existing comparison algorithms on the majority of the test functions.