进化扩散优化。2性能评估

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI:10.1109/CEC.2002.1004428

K. Tsui, Jiming Liu

{"title":"进化扩散优化。2性能评估","authors":"K. Tsui, Jiming Liu","doi":"10.1109/CEC.2002.1004428","DOIUrl":null,"url":null,"abstract":"A new population-based stochastic search algorithm called evolutionary diffusion optimization (EDO) inspired by diffusion in nature has been proposed. This article compares the performance of EDO with simulated annealing and fast evolutionary programming. Experimental results show that EDO performs better than SA and FEP in some cases.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Evolutionary diffusion optimization. II. Performance assessment\",\"authors\":\"K. Tsui, Jiming Liu\",\"doi\":\"10.1109/CEC.2002.1004428\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new population-based stochastic search algorithm called evolutionary diffusion optimization (EDO) inspired by diffusion in nature has been proposed. This article compares the performance of EDO with simulated annealing and fast evolutionary programming. Experimental results show that EDO performs better than SA and FEP in some cases.\",\"PeriodicalId\":184547,\"journal\":{\"name\":\"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-05-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2002.1004428\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2002.1004428","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 3

摘要

受自然界扩散的启发，提出了一种基于种群的随机搜索算法——进化扩散优化算法(EDO)。本文比较了模拟退火算法和快速进化规划算法的性能。实验结果表明，在某些情况下，EDO的性能优于SA和FEP。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Evolutionary diffusion optimization. II. Performance assessment

A new population-based stochastic search algorithm called evolutionary diffusion optimization (EDO) inspired by diffusion in nature has been proposed. This article compares the performance of EDO with simulated annealing and fast evolutionary programming. Experimental results show that EDO performs better than SA and FEP in some cases.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)

自引率

0.00%

发文量