Hossam M. Alsaket, K. Mahmoud, H. Elattar, M. Aboul-Dahab
{"title":"基于多目标粒子群系统的认知网络资源优化","authors":"Hossam M. Alsaket, K. Mahmoud, H. Elattar, M. Aboul-Dahab","doi":"10.1109/WOCC.2017.7928994","DOIUrl":null,"url":null,"abstract":"Recently, Cognitive network has drawn the attention as a promising technology to enhance communication system performance by efficiently utilizing system resources. It provides prompt response to dynamic changes. In this paper, a modified multi-objective particle swarm optimization (M-MOPSO) is proposed in Cognitive IP Multimedia Subsystem (CogIMS) to improve the global network performance. The implementation and evaluation results of the system design using the algorithm is provided and compared with those obtained using Non-Dominated Sorting Genetic Algorithm (NSGA-II). Extensive simulations are carried out by using MATLAB software showed that M-MOPSO is comparable to NSGA-II in the network throughput. However, on average, M-MOPSO is faster than NSGA-II by 6.25 times considering the needed computation time for algorithm convergence.","PeriodicalId":6471,"journal":{"name":"2017 26th Wireless and Optical Communication Conference (WOCC)","volume":"6 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Resource optimizer for Cognitive Network using multi-objective particle swarm system\",\"authors\":\"Hossam M. Alsaket, K. Mahmoud, H. Elattar, M. Aboul-Dahab\",\"doi\":\"10.1109/WOCC.2017.7928994\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recently, Cognitive network has drawn the attention as a promising technology to enhance communication system performance by efficiently utilizing system resources. It provides prompt response to dynamic changes. In this paper, a modified multi-objective particle swarm optimization (M-MOPSO) is proposed in Cognitive IP Multimedia Subsystem (CogIMS) to improve the global network performance. The implementation and evaluation results of the system design using the algorithm is provided and compared with those obtained using Non-Dominated Sorting Genetic Algorithm (NSGA-II). Extensive simulations are carried out by using MATLAB software showed that M-MOPSO is comparable to NSGA-II in the network throughput. However, on average, M-MOPSO is faster than NSGA-II by 6.25 times considering the needed computation time for algorithm convergence.\",\"PeriodicalId\":6471,\"journal\":{\"name\":\"2017 26th Wireless and Optical Communication Conference (WOCC)\",\"volume\":\"6 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 26th Wireless and Optical Communication Conference (WOCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WOCC.2017.7928994\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 26th Wireless and Optical Communication Conference (WOCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WOCC.2017.7928994","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Resource optimizer for Cognitive Network using multi-objective particle swarm system
Recently, Cognitive network has drawn the attention as a promising technology to enhance communication system performance by efficiently utilizing system resources. It provides prompt response to dynamic changes. In this paper, a modified multi-objective particle swarm optimization (M-MOPSO) is proposed in Cognitive IP Multimedia Subsystem (CogIMS) to improve the global network performance. The implementation and evaluation results of the system design using the algorithm is provided and compared with those obtained using Non-Dominated Sorting Genetic Algorithm (NSGA-II). Extensive simulations are carried out by using MATLAB software showed that M-MOPSO is comparable to NSGA-II in the network throughput. However, on average, M-MOPSO is faster than NSGA-II by 6.25 times considering the needed computation time for algorithm convergence.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
