{"title":"Organizational evolutionary applied on geometric constraints solving","authors":"Duo Wang, Wenhui Li, Rongqing Yi, X. Cheng","doi":"10.1109/UKRICIS.2008.4798970","DOIUrl":null,"url":null,"abstract":"In this paper, a new optimization method, Organizational Evolutionary Algorithm (OEA), is proposed, in which a population is made of organizations and whose evolution is led by three organizational evolutionary operators, i.e. the splitting operator, the merging operator and the cooperating operator; the splitting operator controls the size of organizations and make part of organizations enter into next generation directly, which are benefit for keeping the diversity of populations; the merging operator acts as a local searching function with taking advantage of leaders information; the cooperating operator increases the adaptability degree through the interactions of organizations. OEA is successfully applied to solve the non-linear parameterization design problems. Experiments show that OEA performs better than original Generic algorithm (GA) in this application of parameterization design.","PeriodicalId":311925,"journal":{"name":"2008 7th IEEE International Conference on Cybernetic Intelligent Systems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 7th IEEE International Conference on Cybernetic Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UKRICIS.2008.4798970","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, a new optimization method, Organizational Evolutionary Algorithm (OEA), is proposed, in which a population is made of organizations and whose evolution is led by three organizational evolutionary operators, i.e. the splitting operator, the merging operator and the cooperating operator; the splitting operator controls the size of organizations and make part of organizations enter into next generation directly, which are benefit for keeping the diversity of populations; the merging operator acts as a local searching function with taking advantage of leaders information; the cooperating operator increases the adaptability degree through the interactions of organizations. OEA is successfully applied to solve the non-linear parameterization design problems. Experiments show that OEA performs better than original Generic algorithm (GA) in this application of parameterization design.