{"title":"The differential Ant-Stigmergy Algorithm for large-scale global optimization","authors":"P. Korošec, Katerina Tashkova, J. Silc","doi":"10.1109/CEC.2010.5586201","DOIUrl":null,"url":null,"abstract":"Ant-colony optimization (ACO) is a popular swarm intelligence metaheuristic scheme that can be applied to almost any optimization problem. In this paper, we address a performance evaluation of an ACO-based algorithm for solving large-scale global optimization problems with continuous variables, labeled Differential Ant-Stigmergy Algorithm (DASA). The DASA transforms a real-parameter optimization problem into a graph-search problem. The parameters' differences assigned to the graph vertices are used to navigate through the search space. The performance of the DASA is evaluated on the set of benchmark problems provided for CEC'2010 Special Session and Competition on Large-Scale Global Optimization.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Congress on Evolutionary Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2010.5586201","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21
Abstract
Ant-colony optimization (ACO) is a popular swarm intelligence metaheuristic scheme that can be applied to almost any optimization problem. In this paper, we address a performance evaluation of an ACO-based algorithm for solving large-scale global optimization problems with continuous variables, labeled Differential Ant-Stigmergy Algorithm (DASA). The DASA transforms a real-parameter optimization problem into a graph-search problem. The parameters' differences assigned to the graph vertices are used to navigate through the search space. The performance of the DASA is evaluated on the set of benchmark problems provided for CEC'2010 Special Session and Competition on Large-Scale Global Optimization.
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