{"title":"基于混合遗传算法的多目标优化研究","authors":"Hua Jiang, GuiLin Xu, Zhenrong Deng","doi":"10.1109/NCM.2009.136","DOIUrl":null,"url":null,"abstract":"In the process of solving multi-objective Pareto solution, the search ability in total area and the convergence characteristics can be reinforced by self-adjusting of aberrance probability in offspring evolution. Comparing with the typical hybrid genetic algorithm, the more effective optimization convergence can be obtained by using the improved hybrid genetic algorithm in solution for optimization problem. Numerical simulation based on some typical examples demonstrate the effectiveness of the proposed method.","PeriodicalId":119669,"journal":{"name":"2009 Fifth International Joint Conference on INC, IMS and IDC","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Research of Multi-objective Optimization Based on Hybrid Genetic Algorithm\",\"authors\":\"Hua Jiang, GuiLin Xu, Zhenrong Deng\",\"doi\":\"10.1109/NCM.2009.136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the process of solving multi-objective Pareto solution, the search ability in total area and the convergence characteristics can be reinforced by self-adjusting of aberrance probability in offspring evolution. Comparing with the typical hybrid genetic algorithm, the more effective optimization convergence can be obtained by using the improved hybrid genetic algorithm in solution for optimization problem. Numerical simulation based on some typical examples demonstrate the effectiveness of the proposed method.\",\"PeriodicalId\":119669,\"journal\":{\"name\":\"2009 Fifth International Joint Conference on INC, IMS and IDC\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Fifth International Joint Conference on INC, IMS and IDC\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NCM.2009.136\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Fifth International Joint Conference on INC, IMS and IDC","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NCM.2009.136","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research of Multi-objective Optimization Based on Hybrid Genetic Algorithm
In the process of solving multi-objective Pareto solution, the search ability in total area and the convergence characteristics can be reinforced by self-adjusting of aberrance probability in offspring evolution. Comparing with the typical hybrid genetic algorithm, the more effective optimization convergence can be obtained by using the improved hybrid genetic algorithm in solution for optimization problem. Numerical simulation based on some typical examples demonstrate the effectiveness of the proposed method.
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