Applying Genetic Algorithms to the Data Traffic Scheduling and Performance Analysis of a Long-Term Evolution System

[[abstract]]In this study it develops a superior transmission resource allocation method by using genetic algorithm. The convergence properties of genetic algorithm are employed to increase the transmission resource use efficiency of a base (station) to allow users to access wider bandwidth and to improve the system throughput and packet service rate. In this paper, it also studies the genetic algorithm convergent phenomena. The calculated system convergent time is significantly less than that of a long term evolution (LTE) frame duration. Finally, the system performances with and without implementing the genetic algorithm in resource allocations are simulated; their performances are compared to study the effectiveness of using the genetic algorithm in resource allocation.[[notice]]補正完畢[[booktype]]電子

Applying Genetic Algorithms to the Data Traffic Scheduling and Performance Analysis of a Long- Term Evolution System

[[abstract]]This study developed a superior transmission resource allocation method using genetic algorithms. The convergence properties of genetic algorithms were employed to increase the transmission resource use efficiency of a base station, allowing users to access wider bandwidths and improving the system throughput and packet service rates of a multicarrier operation. This study also determined the genetic algorithm convergence time and found that the convergence time required for actual calculation was significantly less than one radio frame duration. Finally, the resource allocation results were simulated with and without the genetic algorithm to compare the performance differences.[[conferencetype]]國際[[conferencedate]]20121101~20121107[[booktype]]電子版 紙本[[iscallforpapers]]Y[[conferencelocation]]新北市, 臺

An efficient ant colony system based on receding horizon control for the aircraft arrival sequencing and scheduling problem

The aircraft arrival sequencing and scheduling (ASS) problem is a salient problem in air traffic control (ATC), which proves to be nondeterministic polynomial (NP) hard. This paper formulates the ASS problem in the form of a permutation problem and proposes a new solution framework that makes the first attempt at using an ant colony system (ACS) algorithm based on the receding horizon control (RHC) to solve it. The resultant RHC-improved ACS algorithm for the ASS problem (termed the RHC-ACS-ASS algorithm) is robust, effective, and efficient, not only due to that the ACS algorithm has a strong global search ability and has been proven to be suitable for these kinds of NP-hard problems but also due to that the RHC technique can divide the problem with receding time windows to reduce the computational burden and enhance the solution's quality. The RHC-ACS-ASS algorithm is extensively tested on the cases from the literatures and the cases randomly generated. Comprehensive investigations are also made for the evaluation of the influences of ACS and RHC parameters on the performance of the algorithm. Moreover, the proposed algorithm is further enhanced by using a two-opt exchange heuristic local search. Experimental results verify that the proposed RHC-ACS-ASS algorithm generally outperforms ordinary ACS without using the RHC technique and genetic algorithms (GAs) in solving the ASS problems and offers high robustness, effectiveness, and efficienc

Using network calculus to optimize the AFDX network

This paper presents quantitative results we obtained when optimizing the setting of priorities of the AFDX traffic flows, with the objective to obtain tighter latency and queue-size deterministic bounds (those bounds are calculated by our Network Calculus tool). We first point out the fact that setting randomly the priorities gives worse bounds than using no priorities, and we then show experiments on the basis of classic optimization techniques such as a descent method and a tentative AlphaBetaassisted brute-force approach: both of them haven’t brought significantly better results. We finally present experiments based on genetic algorithms, and we show how driving these algorithms in an adequate way has allowed us to deliver a full range of priority configurations that bring tighter bounds and allow the network traffic designer to trade off average gains of 40% on all the latency bounds against focused improvement on the largest queue-size bound (up to a 30% reduction)

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

A brief network analysis of Artificial Intelligence publication

In this paper, we present an illustration to the history of Artificial Intelligence(AI) with a statistical analysis of publish since 1940. We collected and mined through the IEEE publish data base to analysis the geological and chronological variance of the activeness of research in AI. The connections between different institutes are showed. The result shows that the leading community of AI research are mainly in the USA, China, the Europe and Japan. The key institutes, authors and the research hotspots are revealed. It is found that the research institutes in the fields like Data Mining, Computer Vision, Pattern Recognition and some other fields of Machine Learning are quite consistent, implying a strong interaction between the community of each field. It is also showed that the research of Electronic Engineering and Industrial or Commercial applications are very active in California. Japan is also publishing a lot of papers in robotics. Due to the limitation of data source, the result might be overly influenced by the number of published articles, which is to our best improved by applying network keynode analysis on the research community instead of merely count the number of publish.Comment: 18 pages, 7 figure