{"title":"PSOSA:一种求解城市规划问题的优化粒子群技术","authors":"W. Al-Hassan, M. Fayek, S. Shaheen","doi":"10.1109/ICCES.2006.320481","DOIUrl":null,"url":null,"abstract":"This paper introduces an optimized particle swarm technique (PSOSA) that uses simulated annealing for optimizing the inertia weight. To study the performance of the proposed technique, it has been applied on the urban planning problem involving a multi-objective fitness function that includes non-overlapping constraints as well as relative positioning requirements. Results show that the proposed technique performs much better as regards convergence speed as well as sustainability to increased load of growing number of blocks to be fitted in the urban planning problem","PeriodicalId":261853,"journal":{"name":"2006 International Conference on Computer Engineering and Systems","volume":"88 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"50","resultStr":"{\"title\":\"PSOSA: An Optimized Particle Swarm Technique for Solving the Urban Planning Problem\",\"authors\":\"W. Al-Hassan, M. Fayek, S. Shaheen\",\"doi\":\"10.1109/ICCES.2006.320481\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces an optimized particle swarm technique (PSOSA) that uses simulated annealing for optimizing the inertia weight. To study the performance of the proposed technique, it has been applied on the urban planning problem involving a multi-objective fitness function that includes non-overlapping constraints as well as relative positioning requirements. Results show that the proposed technique performs much better as regards convergence speed as well as sustainability to increased load of growing number of blocks to be fitted in the urban planning problem\",\"PeriodicalId\":261853,\"journal\":{\"name\":\"2006 International Conference on Computer Engineering and Systems\",\"volume\":\"88 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"50\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 International Conference on Computer Engineering and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCES.2006.320481\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 International Conference on Computer Engineering and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCES.2006.320481","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PSOSA: An Optimized Particle Swarm Technique for Solving the Urban Planning Problem
This paper introduces an optimized particle swarm technique (PSOSA) that uses simulated annealing for optimizing the inertia weight. To study the performance of the proposed technique, it has been applied on the urban planning problem involving a multi-objective fitness function that includes non-overlapping constraints as well as relative positioning requirements. Results show that the proposed technique performs much better as regards convergence speed as well as sustainability to increased load of growing number of blocks to be fitted in the urban planning problem