{"title":"基于moea的模型参数超排序启发方法的鲁棒性分析","authors":"Edgar Covantes Osuna, E. Fernández, Jorge Navarro","doi":"10.1109/ICEEE.2013.6676017","DOIUrl":null,"url":null,"abstract":"To set the parameter values for the outranking model is usually a demanding task for the decision-maker (DM) because it is necessary to provide a large number of parameters (thresholds and weights). The use of indirect methods (preference-disaggregation analysis (PDA)) to infer the set of parameters from a battery of decision examples allows the DM to avoid the task of specifying these values. In this paper a robustness analysis has been made to a PDA method based on an evolutionary approach. In this case we use THESEUS method as an artificial DM and its assignment rule for evaluating each individual of a multi-objective evolutionary algorithm (MOEA) population. The non-dominated solutions are acceptable parameter settlements.","PeriodicalId":226547,"journal":{"name":"2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Robustness analysis of a MOEA-based elicitation method for outranking model parameters\",\"authors\":\"Edgar Covantes Osuna, E. Fernández, Jorge Navarro\",\"doi\":\"10.1109/ICEEE.2013.6676017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To set the parameter values for the outranking model is usually a demanding task for the decision-maker (DM) because it is necessary to provide a large number of parameters (thresholds and weights). The use of indirect methods (preference-disaggregation analysis (PDA)) to infer the set of parameters from a battery of decision examples allows the DM to avoid the task of specifying these values. In this paper a robustness analysis has been made to a PDA method based on an evolutionary approach. In this case we use THESEUS method as an artificial DM and its assignment rule for evaluating each individual of a multi-objective evolutionary algorithm (MOEA) population. The non-dominated solutions are acceptable parameter settlements.\",\"PeriodicalId\":226547,\"journal\":{\"name\":\"2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEEE.2013.6676017\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEE.2013.6676017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Robustness analysis of a MOEA-based elicitation method for outranking model parameters
To set the parameter values for the outranking model is usually a demanding task for the decision-maker (DM) because it is necessary to provide a large number of parameters (thresholds and weights). The use of indirect methods (preference-disaggregation analysis (PDA)) to infer the set of parameters from a battery of decision examples allows the DM to avoid the task of specifying these values. In this paper a robustness analysis has been made to a PDA method based on an evolutionary approach. In this case we use THESEUS method as an artificial DM and its assignment rule for evaluating each individual of a multi-objective evolutionary algorithm (MOEA) population. The non-dominated solutions are acceptable parameter settlements.