一种改进的连续优化引力搜索算法

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI:10.1145/2598394.2602281

E. H. V. Segundo, Gabriel Fiori Neto, A. M. D. Silva, V. Mariani, L. Coelho

{"title":"一种改进的连续优化引力搜索算法","authors":"E. H. V. Segundo, Gabriel Fiori Neto, A. M. D. Silva, V. Mariani, L. Coelho","doi":"10.1145/2598394.2602281","DOIUrl":null,"url":null,"abstract":"The gravitational search algorithm (GSA) is a stochastic population-based metaheuristic inspired by the interaction of masses via Newtonian gravity law. In this paper, we propose a modified GSA (MGSA) based on logarithm and Gaussian signals for enhancing the performance of standard GSA. To evaluate the performance of the proposed MGSA, well-known benchmark functions in the literature are optimized using the proposed MGSA, and provides comparisons with the standard GSA.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"190 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A modified gravitational search algorithm for continuous optimization\",\"authors\":\"E. H. V. Segundo, Gabriel Fiori Neto, A. M. D. Silva, V. Mariani, L. Coelho\",\"doi\":\"10.1145/2598394.2602281\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The gravitational search algorithm (GSA) is a stochastic population-based metaheuristic inspired by the interaction of masses via Newtonian gravity law. In this paper, we propose a modified GSA (MGSA) based on logarithm and Gaussian signals for enhancing the performance of standard GSA. To evaluate the performance of the proposed MGSA, well-known benchmark functions in the literature are optimized using the proposed MGSA, and provides comparisons with the standard GSA.\",\"PeriodicalId\":298232,\"journal\":{\"name\":\"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation\",\"volume\":\"190 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2598394.2602281\",\"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 Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2598394.2602281","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

摘要

重力搜索算法(GSA)是一种基于随机种群的基于牛顿引力定律的质量相互作用的元启发式算法。在本文中，我们提出了一种基于对数和高斯信号的改进GSA (MGSA)，以提高标准GSA的性能。为了评估所提出的MGSA的性能，使用所提出的MGSA对文献中的知名基准函数进行了优化，并与标准GSA进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A modified gravitational search algorithm for continuous optimization

The gravitational search algorithm (GSA) is a stochastic population-based metaheuristic inspired by the interaction of masses via Newtonian gravity law. In this paper, we propose a modified GSA (MGSA) based on logarithm and Gaussian signals for enhancing the performance of standard GSA. To evaluate the performance of the proposed MGSA, well-known benchmark functions in the literature are optimized using the proposed MGSA, and provides comparisons with the standard GSA.

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 Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

自引率

0.00%

发文量