{"title":"基于模拟退火和遗传算法的品牌推广最优选址研究","authors":"L. Tong","doi":"10.1145/3598438.3598440","DOIUrl":null,"url":null,"abstract":"This article introduces the application of the simulated annealing algorithm (SA) in solving brand promotion problems. The goal of the brand promotion problem is to find a path that minimizes the distance through all cities. We use the SA algorithm to solve the brand promotion problem, which avoids the trap of local optimal solutions by using a randomized search strategy and an acceptance of inferior solutions strategy. In this article, we apply the SA algorithm to a brand promotion problem instance and compare it with genetic algorithms and greedy algorithms. The experimental results show that the SA algorithm can obtain results close to the optimal solution and has better robustness and faster convergence speed.","PeriodicalId":338722,"journal":{"name":"Proceedings of the 2022 3rd International Symposium on Big Data and Artificial Intelligence","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study of optimal site selection for brand promotion based on simulated annealing and genetic algorithms\",\"authors\":\"L. Tong\",\"doi\":\"10.1145/3598438.3598440\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article introduces the application of the simulated annealing algorithm (SA) in solving brand promotion problems. The goal of the brand promotion problem is to find a path that minimizes the distance through all cities. We use the SA algorithm to solve the brand promotion problem, which avoids the trap of local optimal solutions by using a randomized search strategy and an acceptance of inferior solutions strategy. In this article, we apply the SA algorithm to a brand promotion problem instance and compare it with genetic algorithms and greedy algorithms. The experimental results show that the SA algorithm can obtain results close to the optimal solution and has better robustness and faster convergence speed.\",\"PeriodicalId\":338722,\"journal\":{\"name\":\"Proceedings of the 2022 3rd International Symposium on Big Data and Artificial Intelligence\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2022 3rd International Symposium on Big Data and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3598438.3598440\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 3rd International Symposium on Big Data and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3598438.3598440","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Study of optimal site selection for brand promotion based on simulated annealing and genetic algorithms
This article introduces the application of the simulated annealing algorithm (SA) in solving brand promotion problems. The goal of the brand promotion problem is to find a path that minimizes the distance through all cities. We use the SA algorithm to solve the brand promotion problem, which avoids the trap of local optimal solutions by using a randomized search strategy and an acceptance of inferior solutions strategy. In this article, we apply the SA algorithm to a brand promotion problem instance and compare it with genetic algorithms and greedy algorithms. The experimental results show that the SA algorithm can obtain results close to the optimal solution and has better robustness and faster convergence speed.