{"title":"基于蚁群算法的发电机燃料成本优化","authors":"I. Suryawati, Sagita Rochman","doi":"10.36456/best.vol1.no1.1988","DOIUrl":null,"url":null,"abstract":"Ant Colony Algorithm (ACA) is an optimization algorithm was inspired by ant behavior when searching for the shortest distance from the food center. In this study, ACA is used for power plants with a fuel cost fitness function. ACA can search destinations faster than conventional methods such as Lagrange. In this study ACA used the optimal power flow of six power plants in the Java Bali 500 KV system, the optimization results reduced fuel costs by 23% and Lagrange 17.4% compared to real conditions. \n ","PeriodicalId":120771,"journal":{"name":"BEST : Journal of Applied Electrical, Science, & Technology","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"GENERATOR FUEL COST OPTIMIZATION USING ANT COLONY ALGORITHM\",\"authors\":\"I. Suryawati, Sagita Rochman\",\"doi\":\"10.36456/best.vol1.no1.1988\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ant Colony Algorithm (ACA) is an optimization algorithm was inspired by ant behavior when searching for the shortest distance from the food center. In this study, ACA is used for power plants with a fuel cost fitness function. ACA can search destinations faster than conventional methods such as Lagrange. In this study ACA used the optimal power flow of six power plants in the Java Bali 500 KV system, the optimization results reduced fuel costs by 23% and Lagrange 17.4% compared to real conditions. \\n \",\"PeriodicalId\":120771,\"journal\":{\"name\":\"BEST : Journal of Applied Electrical, Science, & Technology\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BEST : Journal of Applied Electrical, Science, & Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.36456/best.vol1.no1.1988\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BEST : Journal of Applied Electrical, Science, & Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36456/best.vol1.no1.1988","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
GENERATOR FUEL COST OPTIMIZATION USING ANT COLONY ALGORITHM
Ant Colony Algorithm (ACA) is an optimization algorithm was inspired by ant behavior when searching for the shortest distance from the food center. In this study, ACA is used for power plants with a fuel cost fitness function. ACA can search destinations faster than conventional methods such as Lagrange. In this study ACA used the optimal power flow of six power plants in the Java Bali 500 KV system, the optimization results reduced fuel costs by 23% and Lagrange 17.4% compared to real conditions.
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