{"title":"基于量子遗传算法的改进粒子群算法在水下路径规划中的应用","authors":"Fei Yu, Yang Liu","doi":"10.1109/ISA.2011.5873381","DOIUrl":null,"url":null,"abstract":"An improved particle swarm optimization algorithm (PSO) combined with quantum genetic algorithm is proposed, to solve the problems that the PSO is difficult to converge for benchmark complex problems and it's parameters are hard to define. The new algorithm is used for submersible path planning and simulation on some standard test functions. The results show that the improved is superior to the standard PSO in optimization ability and the convergence rate, and it can find the optimal path faster.","PeriodicalId":128163,"journal":{"name":"2011 3rd International Workshop on Intelligent Systems and Applications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The Application of an Improved PSO Based on the Quantum Genetic Algorithm in the Submersible Path-Planning\",\"authors\":\"Fei Yu, Yang Liu\",\"doi\":\"10.1109/ISA.2011.5873381\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An improved particle swarm optimization algorithm (PSO) combined with quantum genetic algorithm is proposed, to solve the problems that the PSO is difficult to converge for benchmark complex problems and it's parameters are hard to define. The new algorithm is used for submersible path planning and simulation on some standard test functions. The results show that the improved is superior to the standard PSO in optimization ability and the convergence rate, and it can find the optimal path faster.\",\"PeriodicalId\":128163,\"journal\":{\"name\":\"2011 3rd International Workshop on Intelligent Systems and Applications\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 3rd International Workshop on Intelligent Systems and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISA.2011.5873381\",\"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 3rd International Workshop on Intelligent Systems and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISA.2011.5873381","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Application of an Improved PSO Based on the Quantum Genetic Algorithm in the Submersible Path-Planning
An improved particle swarm optimization algorithm (PSO) combined with quantum genetic algorithm is proposed, to solve the problems that the PSO is difficult to converge for benchmark complex problems and it's parameters are hard to define. The new algorithm is used for submersible path planning and simulation on some standard test functions. The results show that the improved is superior to the standard PSO in optimization ability and the convergence rate, and it can find the optimal path faster.
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