大规模TSP交互优化的多世界智能遗传算法

2006 IEEE International Conference on Information Reuse & Integration Pub Date : 2006-12-04 DOI:10.1109/IRI.2006.252421

Yoshitaka Sakurai, T. Onoyama, S. Kubota, Yoshihiro Nakamura, S. Tsuruta

{"title":"大规模TSP交互优化的多世界智能遗传算法","authors":"Yoshitaka Sakurai, T. Onoyama, S. Kubota, Yoshihiro Nakamura, S. Tsuruta","doi":"10.1109/IRI.2006.252421","DOIUrl":null,"url":null,"abstract":"To optimize large-scale distribution networks, solving about 1000 middle scale (around 40 cities) TSPs (traveling salesman problems) within an interactive length of time (max. 30 seconds) is required. Yet, expert-level (less than 3% of errors) accuracy is necessary. To realize the above requirements, a multi-world intelligent GA method was developed. This method combines a high-speed GA with an intelligent GA holding problem-oriented knowledge that is effective for some special location patterns. If conventional methods were applied, solutions for more than 20 out of 20,000 cases were below expert-level accuracy. However, the developed method could solve all of 20,000 cases at expert-level","PeriodicalId":402255,"journal":{"name":"2006 IEEE International Conference on Information Reuse & Integration","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"A Multi-world Intelligent Genetic Algorithm to Interactively Optimize Large-scale TSP\",\"authors\":\"Yoshitaka Sakurai, T. Onoyama, S. Kubota, Yoshihiro Nakamura, S. Tsuruta\",\"doi\":\"10.1109/IRI.2006.252421\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To optimize large-scale distribution networks, solving about 1000 middle scale (around 40 cities) TSPs (traveling salesman problems) within an interactive length of time (max. 30 seconds) is required. Yet, expert-level (less than 3% of errors) accuracy is necessary. To realize the above requirements, a multi-world intelligent GA method was developed. This method combines a high-speed GA with an intelligent GA holding problem-oriented knowledge that is effective for some special location patterns. If conventional methods were applied, solutions for more than 20 out of 20,000 cases were below expert-level accuracy. However, the developed method could solve all of 20,000 cases at expert-level\",\"PeriodicalId\":402255,\"journal\":{\"name\":\"2006 IEEE International Conference on Information Reuse & Integration\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE International Conference on Information Reuse & Integration\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IRI.2006.252421\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE International Conference on Information Reuse & Integration","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRI.2006.252421","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 19

摘要

优化大规模分销网络，在交互时间长度(最大数小时)内解决约1000个中等规模(约40个城市)的tsp(旅行商问题)。30秒)是必需的。然而，专家级(少于3%的错误)的准确性是必要的。为实现上述要求，提出了一种多世界智能遗传算法。该方法将高速遗传算法与具有问题导向知识的智能遗传算法相结合，对某些特殊的定位模式有效。如果采用传统方法，20000个案例中有超过20个的解决方案低于专家水平的准确性。然而，所开发的方法可以在专家水平上解决所有20,000个案例

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 Multi-world Intelligent Genetic Algorithm to Interactively Optimize Large-scale TSP

To optimize large-scale distribution networks, solving about 1000 middle scale (around 40 cities) TSPs (traveling salesman problems) within an interactive length of time (max. 30 seconds) is required. Yet, expert-level (less than 3% of errors) accuracy is necessary. To realize the above requirements, a multi-world intelligent GA method was developed. This method combines a high-speed GA with an intelligent GA holding problem-oriented knowledge that is effective for some special location patterns. If conventional methods were applied, solutions for more than 20 out of 20,000 cases were below expert-level accuracy. However, the developed method could solve all of 20,000 cases at expert-level

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

2006 IEEE International Conference on Information Reuse & Integration

自引率

0.00%

发文量