Dan Wang, Congcong Xiong, Xiankun Zhang, Jianhua Cao, H. Xu
{"title":"A ranking-based space search algorithm with control parameters","authors":"Dan Wang, Congcong Xiong, Xiankun Zhang, Jianhua Cao, H. Xu","doi":"10.1109/ICSESS.2017.8343008","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a ranking-based space search algorithm (RSSA) with the aid of control parameters, which have a significant influence on the performance of the space search algorithm. Ranking strategy is realized in the design of space search operators. Also, instead of fixed parameter, three given values are considered as suitable control parameter settings. To evaluate the performance of the RSSA, 10 well-known benchmark functions are tested. Furthermore, the proposed RSSA is applied as optimal vehicle for a nonlinear data set. Experimental results show the RSSA is very competitive.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSESS.2017.8343008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we propose a ranking-based space search algorithm (RSSA) with the aid of control parameters, which have a significant influence on the performance of the space search algorithm. Ranking strategy is realized in the design of space search operators. Also, instead of fixed parameter, three given values are considered as suitable control parameter settings. To evaluate the performance of the RSSA, 10 well-known benchmark functions are tested. Furthermore, the proposed RSSA is applied as optimal vehicle for a nonlinear data set. Experimental results show the RSSA is very competitive.