{"title":"基于信任域算法的局部搜索多目标优化","authors":"A. El-sawy, Z. M. Hendawy, M. El-Shorbagy","doi":"10.1109/ICIES.2012.6530871","DOIUrl":null,"url":null,"abstract":"In this paper, a new algorithm is proposed to solve multi-objective optimization problems (MOOPs) through applying the trust-region (TR) method based local search (LS) techniques; where the MOOP converting to a single objective optimization problem (SOOP) by using reference point method. In the proposed algorithm, for each reference point the TR algorithm for solving a SOOP is used to obtain a point on the Pareto frontier. In addition a LS method is used, in order to find more points on the Pareto frontier. The algorithm is coded in MATLAB 7.2 and the simulations are run on a Pentium 4 CPU 900 MHz with 512 MB memory capacity. The numerical results show that the proposed method is feasible, and illustrate the ability of finding an approximation of Pareto optimal set.","PeriodicalId":410182,"journal":{"name":"2012 First International Conference on Innovative Engineering Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Trust-region algorithm based local search for multi-objective optimization\",\"authors\":\"A. El-sawy, Z. M. Hendawy, M. El-Shorbagy\",\"doi\":\"10.1109/ICIES.2012.6530871\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a new algorithm is proposed to solve multi-objective optimization problems (MOOPs) through applying the trust-region (TR) method based local search (LS) techniques; where the MOOP converting to a single objective optimization problem (SOOP) by using reference point method. In the proposed algorithm, for each reference point the TR algorithm for solving a SOOP is used to obtain a point on the Pareto frontier. In addition a LS method is used, in order to find more points on the Pareto frontier. The algorithm is coded in MATLAB 7.2 and the simulations are run on a Pentium 4 CPU 900 MHz with 512 MB memory capacity. The numerical results show that the proposed method is feasible, and illustrate the ability of finding an approximation of Pareto optimal set.\",\"PeriodicalId\":410182,\"journal\":{\"name\":\"2012 First International Conference on Innovative Engineering Systems\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 First International Conference on Innovative Engineering Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIES.2012.6530871\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 First International Conference on Innovative Engineering Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIES.2012.6530871","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Trust-region algorithm based local search for multi-objective optimization
In this paper, a new algorithm is proposed to solve multi-objective optimization problems (MOOPs) through applying the trust-region (TR) method based local search (LS) techniques; where the MOOP converting to a single objective optimization problem (SOOP) by using reference point method. In the proposed algorithm, for each reference point the TR algorithm for solving a SOOP is used to obtain a point on the Pareto frontier. In addition a LS method is used, in order to find more points on the Pareto frontier. The algorithm is coded in MATLAB 7.2 and the simulations are run on a Pentium 4 CPU 900 MHz with 512 MB memory capacity. The numerical results show that the proposed method is feasible, and illustrate the ability of finding an approximation of Pareto optimal set.