{"title":"A new Hybrid Fuzzy Dynamic Velocity Feedback PSO for non-convex economic dispatch problem","authors":"E. Muneender, D. Vinodkumar","doi":"10.1109/STUDENT.2012.6408386","DOIUrl":null,"url":null,"abstract":"This paper proposes a new Hybrid Fuzzy Dynamic Velocity Feedback Particle Swarm Optimization (HFDVF-PSO) for solving Economic Dispatch (ED) problem with non-smooth cost functions considering valve-point effects and multiple fuel options. In the proposed HFDVF-PSO method, the inertia weight is dynamically and nonlinearly adjusted to obtain better balance between global and local search abilities of the PSO using the absolute value of the average velocity of the particles as a feedback to the fuzzy inference system. The performance of the proposed method is tested on standard 10-unit test system and is compared with the conventional PSO method and the methods reported in literature. The simulation results reveal that the proposed HFDVF-PSO method out performs the conventional PSO and other Evolutionary Algorithms (EA) reported in literature.","PeriodicalId":282263,"journal":{"name":"2012 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STUDENT.2012.6408386","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper proposes a new Hybrid Fuzzy Dynamic Velocity Feedback Particle Swarm Optimization (HFDVF-PSO) for solving Economic Dispatch (ED) problem with non-smooth cost functions considering valve-point effects and multiple fuel options. In the proposed HFDVF-PSO method, the inertia weight is dynamically and nonlinearly adjusted to obtain better balance between global and local search abilities of the PSO using the absolute value of the average velocity of the particles as a feedback to the fuzzy inference system. The performance of the proposed method is tested on standard 10-unit test system and is compared with the conventional PSO method and the methods reported in literature. The simulation results reveal that the proposed HFDVF-PSO method out performs the conventional PSO and other Evolutionary Algorithms (EA) reported in literature.