一种改进的量子粒子群算法及其应用

2017 6th International Conference on Computer Science and Network Technology (ICCSNT) Pub Date : 2017-10-01 DOI:10.1109/ICCSNT.2017.8343471

Jiao Xuan, Huang Ming

{"title":"一种改进的量子粒子群算法及其应用","authors":"Jiao Xuan, Huang Ming","doi":"10.1109/ICCSNT.2017.8343471","DOIUrl":null,"url":null,"abstract":"Compared to other intelligent optimization algorithms, Quantum Particle Swarm Optimization (QPSO) possesses the characteristics like rapid convergence rate and outstanding global optimization performance etc. It is more applicable to solve workshop scheduling problems. The article proposes the strategy of improved dynamic reglation of rotation angle to solve multi-objective FJSP problems on the basis of Quantum Particle Swarm Optimization. The method can ensure the position with large variation of adaptive value not over optimal regulation measure, increase the capability to search optimal solution at the position with small variation of adaptive value, and verify the effectiveness of new algorithm through simulation experiement.","PeriodicalId":163433,"journal":{"name":"2017 6th International Conference on Computer Science and Network Technology (ICCSNT)","volume":"35 33","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"An improved Quantum Particle Swarm Optimization and its application\",\"authors\":\"Jiao Xuan, Huang Ming\",\"doi\":\"10.1109/ICCSNT.2017.8343471\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Compared to other intelligent optimization algorithms, Quantum Particle Swarm Optimization (QPSO) possesses the characteristics like rapid convergence rate and outstanding global optimization performance etc. It is more applicable to solve workshop scheduling problems. The article proposes the strategy of improved dynamic reglation of rotation angle to solve multi-objective FJSP problems on the basis of Quantum Particle Swarm Optimization. The method can ensure the position with large variation of adaptive value not over optimal regulation measure, increase the capability to search optimal solution at the position with small variation of adaptive value, and verify the effectiveness of new algorithm through simulation experiement.\",\"PeriodicalId\":163433,\"journal\":{\"name\":\"2017 6th International Conference on Computer Science and Network Technology (ICCSNT)\",\"volume\":\"35 33\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 6th International Conference on Computer Science and Network Technology (ICCSNT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSNT.2017.8343471\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 6th International Conference on Computer Science and Network Technology (ICCSNT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSNT.2017.8343471","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

与其他智能优化算法相比，量子粒子群算法具有收敛速度快、全局寻优性能突出等特点。更适用于解决车间调度问题。提出了一种基于量子粒子群优化的改进旋转角度动态调节策略来解决多目标FJSP问题。该方法可以保证自适应值变化较大的位置不超过最优调节措施，增加了在自适应值变化较小位置搜索最优解的能力，并通过仿真实验验证了新算法的有效性。

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

An improved Quantum Particle Swarm Optimization and its application

Compared to other intelligent optimization algorithms, Quantum Particle Swarm Optimization (QPSO) possesses the characteristics like rapid convergence rate and outstanding global optimization performance etc. It is more applicable to solve workshop scheduling problems. The article proposes the strategy of improved dynamic reglation of rotation angle to solve multi-objective FJSP problems on the basis of Quantum Particle Swarm Optimization. The method can ensure the position with large variation of adaptive value not over optimal regulation measure, increase the capability to search optimal solution at the position with small variation of adaptive value, and verify the effectiveness of new algorithm through simulation experiement.

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

2017 6th International Conference on Computer Science and Network Technology (ICCSNT)

自引率

0.00%

发文量