Bao E-er-dun, Wang Xiao-ping, Xue Jian-ping, Liu Qin, Wang Fa-wei
{"title":"QPSO算法在航空发动机性能优化中的应用","authors":"Bao E-er-dun, Wang Xiao-ping, Xue Jian-ping, Liu Qin, Wang Fa-wei","doi":"10.1109/CCIENG.2011.6008146","DOIUrl":null,"url":null,"abstract":"A novel and practical method which is Quantum-behaved Particle Swam Optimization (QPSO) algorithm is applied in one type of turbo fan engine performance optimization. In this paper, by comparison with PSO algorithm, QPSO algorithm have obvious advantages. The simulation is carried out under different altitudes and velocities. The result shows that thrust can be increased by 7% ∼ 9% under maximum thrust mode and improved by 0.3% ∼ 3.7% than that is optimized by Particle Swam Optimization (PSO) algorithm. Meanwhile, fuel consumption can be decreased by 2% ∼ 3% under the minimum fuel consumption mode. The influence of initial values on PSO algorithm is reduced and the problem of being easily trapped in local optimal values is solved as well. Apparently, the algorithm is of great application value.","PeriodicalId":6316,"journal":{"name":"2011 IEEE 2nd International Conference on Computing, Control and Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"QPSO algorithm in aeroengine performance optimization of application\",\"authors\":\"Bao E-er-dun, Wang Xiao-ping, Xue Jian-ping, Liu Qin, Wang Fa-wei\",\"doi\":\"10.1109/CCIENG.2011.6008146\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A novel and practical method which is Quantum-behaved Particle Swam Optimization (QPSO) algorithm is applied in one type of turbo fan engine performance optimization. In this paper, by comparison with PSO algorithm, QPSO algorithm have obvious advantages. The simulation is carried out under different altitudes and velocities. The result shows that thrust can be increased by 7% ∼ 9% under maximum thrust mode and improved by 0.3% ∼ 3.7% than that is optimized by Particle Swam Optimization (PSO) algorithm. Meanwhile, fuel consumption can be decreased by 2% ∼ 3% under the minimum fuel consumption mode. The influence of initial values on PSO algorithm is reduced and the problem of being easily trapped in local optimal values is solved as well. Apparently, the algorithm is of great application value.\",\"PeriodicalId\":6316,\"journal\":{\"name\":\"2011 IEEE 2nd International Conference on Computing, Control and Industrial Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE 2nd International Conference on Computing, Control and Industrial Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCIENG.2011.6008146\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE 2nd International Conference on Computing, Control and Industrial Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCIENG.2011.6008146","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
QPSO algorithm in aeroengine performance optimization of application
A novel and practical method which is Quantum-behaved Particle Swam Optimization (QPSO) algorithm is applied in one type of turbo fan engine performance optimization. In this paper, by comparison with PSO algorithm, QPSO algorithm have obvious advantages. The simulation is carried out under different altitudes and velocities. The result shows that thrust can be increased by 7% ∼ 9% under maximum thrust mode and improved by 0.3% ∼ 3.7% than that is optimized by Particle Swam Optimization (PSO) algorithm. Meanwhile, fuel consumption can be decreased by 2% ∼ 3% under the minimum fuel consumption mode. The influence of initial values on PSO algorithm is reduced and the problem of being easily trapped in local optimal values is solved as well. Apparently, the algorithm is of great application value.
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