{"title":"Real coded clonal selection algorithm for unconstrained global optimization using a hybrid inversely proportional hypermutation operator","authors":"V. Cutello, Giuseppe Nicosia, M. Pavone","doi":"10.1145/1141277.1141501","DOIUrl":null,"url":null,"abstract":"Numerical optimization of given objective functions is a crucial task in many real-life problems. This paper introduces a new immunological algorithm for continuous global optimization problems, called opt-IMMALG; it is an improved version of a previously proposed clonal selection algorithm, using a real-code representation and a new Inversely Proportional Hypermutation operator.We evaluate and assess the performance of opt-IMMALG and several others algorithms, namely opt-IA, PSO, arPSO, DE, and SEA with respect to their general applicability as numerical optimization algorithms. The experiments have been performed on 23 widely used benchmark problems.The experimental results show that opt-IMMALG is a suitable numerical optimization technique that, in terms of accuracy, outperforms the analyzed algorithms in this comparative study. In addition it is shown that opt-IMMALG is also suitable for solving large-scale problems.","PeriodicalId":269830,"journal":{"name":"Proceedings of the 2006 ACM symposium on Applied computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2006-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"75","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2006 ACM symposium on Applied computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1141277.1141501","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 75
Abstract
Numerical optimization of given objective functions is a crucial task in many real-life problems. This paper introduces a new immunological algorithm for continuous global optimization problems, called opt-IMMALG; it is an improved version of a previously proposed clonal selection algorithm, using a real-code representation and a new Inversely Proportional Hypermutation operator.We evaluate and assess the performance of opt-IMMALG and several others algorithms, namely opt-IA, PSO, arPSO, DE, and SEA with respect to their general applicability as numerical optimization algorithms. The experiments have been performed on 23 widely used benchmark problems.The experimental results show that opt-IMMALG is a suitable numerical optimization technique that, in terms of accuracy, outperforms the analyzed algorithms in this comparative study. In addition it is shown that opt-IMMALG is also suitable for solving large-scale problems.