{"title":"绿色建筑背景下基于NSGA-II算法的工程项目多目标优化模型构建","authors":"Fushun Zhang","doi":"10.31181/dmame712024895","DOIUrl":null,"url":null,"abstract":"In the context of Sustainability Development (SD), Green Construction (GC) has become a key direction for optimizing engineering project objectives. In order to improve the management ability of project engineering in GC, an improved NSGA - II algorithm was used in this study to establish a multi-optimization model for engineering projects. In this process, the hill climbing is introduced to improve the search ability of NSGA - Ⅱ algorithm. Finally, a Multi-Objective Optimization (MOP) model with strong convergence and distribution was obtained. In subsequent validation experiments, the total construction period of the engineering project MOP model based on the improved NSGA - II algorithm was between 190 and 234days. The total cost ranges from 171,473 to 20,461,800 yuan. Its total mass ranges from 90.41% to 92.19%. Its total safety is between 91.30% and 99.32%. The total environment is between 144.54 and 193.58. Its total resources range from 86.21% to 99.91%. The cost of improving the NSGA-II algorithm is 500300 yuan lower than that of the NSGA-II algorithm, with a resource target increase of 0.4% and an environmental target increase of 4.33%. The iteration curves of the improved NSGA - II algorithm in terms of duration, cost, and environmental objective function are lower than those of the NSGA - II algorithm. Overall, the improved NSGA - II algorithm has better MOP performance, can obtain better Pareto solutions, and has better performance.","PeriodicalId":32695,"journal":{"name":"Decision Making Applications in Management and Engineering","volume":"122 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Constructing a Multi-Objective Optimization Model for Engineering Projects Based on NSGA-II Algorithm under the Background of Green Construction\",\"authors\":\"Fushun Zhang\",\"doi\":\"10.31181/dmame712024895\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the context of Sustainability Development (SD), Green Construction (GC) has become a key direction for optimizing engineering project objectives. In order to improve the management ability of project engineering in GC, an improved NSGA - II algorithm was used in this study to establish a multi-optimization model for engineering projects. In this process, the hill climbing is introduced to improve the search ability of NSGA - Ⅱ algorithm. Finally, a Multi-Objective Optimization (MOP) model with strong convergence and distribution was obtained. In subsequent validation experiments, the total construction period of the engineering project MOP model based on the improved NSGA - II algorithm was between 190 and 234days. The total cost ranges from 171,473 to 20,461,800 yuan. Its total mass ranges from 90.41% to 92.19%. Its total safety is between 91.30% and 99.32%. The total environment is between 144.54 and 193.58. Its total resources range from 86.21% to 99.91%. The cost of improving the NSGA-II algorithm is 500300 yuan lower than that of the NSGA-II algorithm, with a resource target increase of 0.4% and an environmental target increase of 4.33%. The iteration curves of the improved NSGA - II algorithm in terms of duration, cost, and environmental objective function are lower than those of the NSGA - II algorithm. Overall, the improved NSGA - II algorithm has better MOP performance, can obtain better Pareto solutions, and has better performance.\",\"PeriodicalId\":32695,\"journal\":{\"name\":\"Decision Making Applications in Management and Engineering\",\"volume\":\"122 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Decision Making Applications in Management and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31181/dmame712024895\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Decision Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Decision Making Applications in Management and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31181/dmame712024895","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Decision Sciences","Score":null,"Total":0}
Constructing a Multi-Objective Optimization Model for Engineering Projects Based on NSGA-II Algorithm under the Background of Green Construction
In the context of Sustainability Development (SD), Green Construction (GC) has become a key direction for optimizing engineering project objectives. In order to improve the management ability of project engineering in GC, an improved NSGA - II algorithm was used in this study to establish a multi-optimization model for engineering projects. In this process, the hill climbing is introduced to improve the search ability of NSGA - Ⅱ algorithm. Finally, a Multi-Objective Optimization (MOP) model with strong convergence and distribution was obtained. In subsequent validation experiments, the total construction period of the engineering project MOP model based on the improved NSGA - II algorithm was between 190 and 234days. The total cost ranges from 171,473 to 20,461,800 yuan. Its total mass ranges from 90.41% to 92.19%. Its total safety is between 91.30% and 99.32%. The total environment is between 144.54 and 193.58. Its total resources range from 86.21% to 99.91%. The cost of improving the NSGA-II algorithm is 500300 yuan lower than that of the NSGA-II algorithm, with a resource target increase of 0.4% and an environmental target increase of 4.33%. The iteration curves of the improved NSGA - II algorithm in terms of duration, cost, and environmental objective function are lower than those of the NSGA - II algorithm. Overall, the improved NSGA - II algorithm has better MOP performance, can obtain better Pareto solutions, and has better performance.