<div> <div> <div> <p>The use of graph-structured data in applications is increasing day by day. In order to infer useful information from such data, fast analytics and software tools are required. One of the graph analytics techniques used is community detection. Community detection is the technique of finding structural communities within a graph. Such communities are defined as groups which have highly connected nodes and have similarities with each other. </p> <p>This research proposes a parallel heuristic for faster community detection using the parallel version of the Louvain algorithm: Grappolo. The Louvain algorithm is a hierarchical algorithm that focuses on modularity optimization. It gained popularity because of its ability to detect high-quality communities faster than the other existing community detection algorithms. However, the Louvain algorithm is a sequential algorithm. To reduce the execution time of the Louvain algorithm, a parallel version named Grappolo exists in the literature. This algorithm proposes parallel heuristics that address the challenges that occur due to parallelizing the sequential Louvain algorithm. </p> <p>In this study, the researcher is investigating if Grappolo can be further parallelized to further reduce the execution time maintaining the quality of communities detected. To evaluate the proposed heuristic, it was tested on an OpenMP multithreaded environment. It was implemented on source codes of Android malware applications. However, as compared to Grapplolo, the proposed modified version resulted in higher execution times for the inputs tested. The modularity of the communities detected was similar to the Grappolo implementation. </p> </div> </div> </div>

In this work, the problem of relay selection and resource power allocation in one- way and two-way cognitive relay networks using half duplex channels with different relaying protocols is investigated. Optimization problems for both single and multiple relay selection that maximize the sum rate of the secondary network without degrading the quality of service of the primary network by respecting a tolerated interference threshold were formulated. Single relay selection and optimal power allocation for two-way relaying cognitive radio networks using decode-and-forward and amplify-and-forward protocols were studied. Dual decomposition and subgradient methods were used to find the optimal power allocation. The transmission process to exchange two different messages between two transceivers for two-way relaying technique takes place in two time slots. In the first slot, the transceivers transmit their signals simultaneously to the relay. Then, during the second slot the relay broadcasts its signal to the terminals. Moreover, improvement of both spectral and energy efficiency can be achieved compared with the one-way relaying technique. As an extension, a multiple relay selection for both one-way and two-way relaying under cognitive radio scenario using amplify-and-forward were discussed. A strong optimization tool based on genetic and iterative algorithms was employed to solve the 
formulated optimization problems for both single and multiple relay selection, where discrete relay power levels were considered. Simulation results show that the practical and low-complexity heuristic approaches achieve almost the same performance of the optimal relay selection schemes either with discrete or continuous power distributions while providing a considerable saving in terms of computational complexity.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23323号 / 情博第759号 / 新制||情||129(附属図書館) / 京都大学大学院情報学研究科数理工学専攻 / (主査)教授 中村 佳正, 教授 矢ヶ崎 一幸, 准教授 辻本 諭 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23548号 / 情博第778号 / 新制||情||132(附属図書館) / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 湊 真一, 教授 山本 章博, 准教授 川原 純 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Thesis describes main attributes and principles of Evolutionary and Genetic algorithms. Crossover, mutation and selection are described as well as termination options. There are examples of practical use of evolutionary and genetic algorithms. Optimization of distribution routes using PHP&MySQL and Google Maps API technologies.

The classic flow shop problem assumes that jobs make only single passes through the processing machines and that the processing times are not affected by the length of the delay before jobs are processed. These assumptions are being relaxed in recent papers that consider reentrance problems and those with schedule deterioration. In this study, these two assumptions are both relaxed, and a model of a reentrant flowshop with a deteriorating schedule is considered. A linear programming formulation of the problem is first presented. Three solution heuristics are considered under different deterioration scenarios. It was observed that both Nawaz Enscor and Ham (NEH) algorithm and Genetic Algorithm (GA) performed much better than the Campbell Dudek and Smith (CDS) algorithm. Overall, when considering both the quality of solution and computational time together, the NEH algorithm seems to have performed much better than the others as the size of problems increases. This model would find useful applications in some metallurgical and manufacturing processes where such problems are usually encountered. / Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2021. / Industrial and Systems Engineering / MEng (Industrial Engineering) / Unrestricted

Thesis describes main attributes and principles of Evolutionary and Genetic algorithms. Crossover, mutation and selection are described as well as termination options. There are examples of practical use of evolutionary and genetic algorithms. Optimization of distribution routes using PHP&MySQL and Google Maps API technologies.

The main aim of this thesis is flow shop and job shop scheduling problem in logistics warehouses. Managing and scheduling works is currently often problem. There is no simple solution due to complexity of this problem. This problem must be resolved because of a lack efficiency of work with a higher load such as during the christmas holidays. This paper describes the methods used to solve this problem focusing mainly on the use of search algorithms, evolutionary algorithms, specifically grammar guided genetic programming. This paper describes the problem of job shop scheduling on a simple theoretical example. The implemented algorithm for solving this problem was subjected to tests inspired on data from real warehouse, as well as synthetically created tests with more jobs and a greater number of workers. Synthetic tests were generated randomly. All tests were therefore run several times and the results were averaged. In conclusion of this work are presented the results of the algorithm and the optimum parameter settings for different sizes of problems and requirements for the solution. Genetic algorithm has been extended to calculate fitness of individuals with regard to number of collisions, extended to use priority rules during run of evolution, and some parts of algorithm was parallelized.

Software implementovaný v rámci Fly-by-Wire systému firmy Honeywell je složen z mnoha modelů implementovaných v systému Simulink. Tato práce se zabývá hledáním přímých a nepřímých spojitostí mezi jednotlivými signály v systému těchto modelů. Jako vhodný aparát je zvolena teorie grafů. Na základě modelů jsou vygenerovány grafy a úloha nalézt spojitost mezi signály je převedena na úlohu nalézt cestu mezi dvěma vrcholy grafu. Známé algoritmy k vyhledávání cest v grafech určují délku nebo váhu nalezené cesty. Pro tuto aplikaci je ale potřeba rozlišovat cesty primární a sekundární bez ohledu na jejich délku. Proto jsou tyto známé algoritmy upraveny.

This diploma thesis deals with genetic algorithms and their properties. Particular emphasis is placed on finding the influence of mutation and population size. Genetic algorithms are applied on inverse heat conduction problems (IHCP) in the second part of the thesis. Several different approaches and coding methods were tested. Properties of genetic algorithms were improved by definition of two new genetic operators – manipulation and sorting. Reported theoretical findings were tested on the real data of inverse heat conduction problem. The library for easy implementation of GA for solving general optimization problems in C ++ was created and is described in the last chapter.