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Efficient Visibility Computation and Simplification in Different Environments
, Ph.D. Dissertation Sharif University of Technology ; Ghodsi, Mohammad (Supervisor)
Abstract
In this thesis, we considered several types of the visibility problem. These problems include computing the visibility polygon of a point observer inside a polygonal domain, maintaining visibility polygon of a moving point observer, visibility coherence in space, maintaining visibility polygon of a moving segment observer and visibility dependent simplification. Furthermore, we considered these problems in both offline and streaming settings. These problems arise in different practical areas, such as computer graphics, machine vision, robotics, motion planning, geographic information systems (GIS) and computational geometry. We obtained effective theoretical results as well as superior...
Simulation and Optimization of Ethane Recovery Unit Using Evolutionary Algorithms and Artificial Neural Networks
, M.Sc. Thesis Sharif University of Technology ; Rashtchian, Davood (Supervisor)
Abstract
Nowadays evolutionary algorithms help developers to solve many problems in applied science and engineering aspects. So the main goal of this thesis is to apply and verify the ability of evolutionary algorithms and artificial neural networks in optimization of Ethane recovery unit. Algorithms applied in this study are Ant Colony Algorithm, Artificial Immune Systems Algorithm, Incremental Evolutionary Algorithm, Chaotic Based Algorithm, Variable Population Size Genetic Algorithm, Frog Leaping Algorithm, Frog Leaping with Bacterial Optimization Approach and Different types of Particle Swarm Optimization Algorithm. Optimization methods based on local search are also applied in order to compare...
A new Optimization Approach for Solving Well Placement Problem under Uncertainty Assessment
, M.Sc. Thesis Sharif University of Technology ; Massihi, Mohsen (Supervisor) ; Roosta Azad, Reza (Supervisor)
Abstract
Determining the best location for new wells is a complex problem that depends on reservoir and fluid properties, well and surface equipment specifications, and economic criteria. Various approaches have been proposed for this problem. Among those, direct optimization using the simulator as the evaluation function, although accurate, is in most cases infeasible due to the number of simulations required. This study proposes a hybrid optimization technique (HGA) based on the genetic algorithm (GA) with helper functions based on the polytope algorithm and the neural network. Hybridization of the GA with these helper methods introduces hill-climbing into the stochastic search and also makes use...
Solving the Bus Rapid Transit Network Design Problem with Ant System Algorithm (in Realistic Urban Network)
, M.Sc. Thesis Sharif University of Technology ; Poorzahedy, Hossein (Supervisor)
Abstract
Network design problem (NDP) is an important problem in transportation planning, in which transit network design is one the most important problems. Public transportation means, however, can not meet urban mobility needs in the developing countries satisfactorily. Bus services are usually unsuitable, insufficient, and unreliable. Bus Rapid Transit (BRT) system, is an emerging public transport alternative designed to provide appropriate, low cost, and effective services, and improve mobility in both developed and developing countries. To design public transportation systems, existing constraints such as resources and budget have to be considered to serve estimated demand properly. Bus Rapid...
Optimizing Long-Term Coordinated Operation of Hydro-Thermal Power Systems using Noisy GA & NSGA-II
, M.Sc. Thesis Sharif University of Technology ; Ardakanian, Reza (Supervisor)
Abstract
Long-term coordinated operation of hydro-thermal power systems has important rule in energy generation planning and management. Therefore a new approach for optimization and long-term planning of hydro-thermal power system is developed. In this research main parameters of the system like inflows and energy are considered as uncertain and so scenario optimization technique is applied. The advantage of this research compare to similar approaches is about applying two objective functions which minimize cost of energy generation and flood control.The Noisy GA and NSGA-II algorithms are used to run the model solving the khouzistan hydro-thermal power system in Iran.The results of this model is...
Joint Disrtibuted Source and Network Coding
, M.Sc. Thesis Sharif University of Technology ; Movaghar Rahimabadi, Ali (Supervisor)
Abstract
Network coding, as a novel technique, suggests that the intermediate nodes of a network can combine independent flows to optimize the usage of a shared communication channel. Also, distributed source coding, exploits the joint statistics of correlated information sources to reduce the volume of transmitted information. Surprisingly, it has been shown that linear network codes are able to compress correlated sources. In this project, we have focused on this problem, i.e. joint distributed source and network coding. We have two important contributions with different directions. First we give a practical design for joint coding. Second, we use an evolutionary approach to find the best placement...
Node Placement in a Wireless Sensor Networks with Genetic Algorithm
, M.Sc. Thesis Sharif University of Technology ; Seifipour, Navid (Supervisor)
Abstract
Coverage rate is a critical criterion in all of communication networks. In this regard, there are many different methods for distribution of network particles duo to attain the most efficient region of coverage. This emerges from the limited ability of each networks particle both in range of communicate and power consumption. In this thesis an evolutionary method is introduced to how Wireless Sensor Networks (WSN) particles can be dispensed to have more reliable coverage rate. Using both static and mobile sensor packages to cover the desired area and used a cost function, coverage rate, the authors tried to maximize it to have an excellent performance of network. Then proposed algorithm is...
Heuristic Hybrid Genetic and Simulated Annealing Algorithms with Neural Networks for Task Assignment in Heterogeneous Computing Systems
, M.Sc. Thesis Sharif University of Technology ; Mahdavi Amiri, Nezamoddin (Supervisor)
Abstract
In this thesis, we want to present methods that are able to solve the assignment tasks problem in a heterogeneous computing system. These methods are two hybrid methods that are constructed by composing Hopefield Neural Networks with Genetic Algorithms and the Simulated Annealing. First, we solve the relaxed problem by applying Genetic Algorithms and the Simulated Annealing and we compare the results of these ways with other traditional methods. Then, we solve the constrained problem with mentioned hybrid methods. The definition of the problem is as following: Consider a distributed computing system which is comprised of set of processors with different speeds but the same structure. We want...
2-D Bone Structure Prediction of Proximal Femur and Dominant Joint Load Estimation using Level Set Method and Bone RemodelingTheories
, M.Sc. Thesis Sharif University of Technology ; Farahmand, Farzam (Supervisor) ; Movahhedy, Mohammad Reza (Supervisor) ; Rouhi, Gholamreza (Co-Advisor)
Abstract
Bones adapt their form and structure to make an efficient use of their mass against the applied mechanical loads. So, it is not surprising to assume that the geometry and density distribution of a bone contains information about its loading history. The objective of this work was to develop a framework to simulate the bone remodeling procedure as a topology optimization process and then use this framework to develop a simple technique for estimating the dominant joint loads based on the bone’s density distribution.
At first, the remodeling equation was derived from the structural optimization task of minimizing the strain energy in each time step, using the level set method. Employment...
At first, the remodeling equation was derived from the structural optimization task of minimizing the strain energy in each time step, using the level set method. Employment...
Solving the P-Center Problem under Uncertainty
, M.Sc. Thesis Sharif University of Technology ; Shavandi, Hassan (Supervisor)
Abstract
Facility location decisions play a prominent role in strategic planning of many firms, companies and governmental organizations. Since in many real-world facility location problems, the data are subject to uncertainty, in this thesis, we consider the P-center problem under uncertainty of demand nodes. Using Bertsimas and Sim approach, we modeled the problem as a linear optimization model. Furthermore, we develop a tabu search algorithm to solve the problem. Finally we designed some experiments to adjust the parameters of tabu search algorithm. We presented the numerical result accordingly
Implementation and Experimentation of a P2P Network for Dynamic Multidimensional Data Structure
, M.Sc. Thesis Sharif University of Technology ; Ghodsi, Mohammad (Supervisor)
Abstract
This thesis presents a dynamic multidimensional data structure, which is called Improved Skip Quadtree for peer-to peer networks. This data structure is the Improved version of Skip Quadtree which originally presented jointly by D.Eppstein, M.T.Goodrich, and J.Z.Sun. It tries to combine the best features of two well-known data structures; hieracical structure search quadtree, and skip list. In this thesis, it is tried to focus on algorithms for inserting and deleting points in both skip quadtree and Improved skip quadtree, which is fast method for performing point location and approximate range queries. The result of experimentation and implementation of this structure shows that it can be...
Approximation Approaches for Ontology Alignment
, M.Sc. Thesis Sharif University of Technology ; Abolhassani, Hassan (Supervisor)
Abstract
Ontology alignment is a process for selection of a good mapping between entities of two (or more) ontologies. It can be viewed as a two phase process of: 1) Find the similarity of each pair of entities from two ontologies, and 2) Extraction of an optimal or near optimal mapping. Many researches had been down in the first phase, but researches in the second phase are little. Mapping extraction approaches applied in ontology alignment use similarity matrix to extract mapping, and ontologies have not any role in mapping extraction phase. One of the most important components in ontology is its structure which can help us in alignment extraction.
In this thesis, an approach was proposed that...
In this thesis, an approach was proposed that...
Minimization of Nonconvex Locally Lipschitz Functions Using Mollifier Subdifferentials and Uniform Approximations of Generalized Second Order Derivative
, Ph.D. Dissertation Sharif University of Technology ; Mahdavi Amiri, Nezameddin (Supervisor)
Abstract
Here, we first ivestigate some available nonsmooth algorithms indentilying their drawbacks, and then provide some guidelines to avert these drawbacks. We divide the algorithms to two main classes, first order and second order. The main drawback of the second order class of algorithm is that there does not exist any suitable method to approximate the generalized second order derivative. To solve this problem, we construct a uniform approximation for the generalized Hessian matrix of an SC1 function. Using the discrete gradient and the extended second order derivative, we define the discrete Hessian matrix. We construct a sequence of sets, where each set is composed of discrete Hessian...
Comparison of the Different Classical Structural Optimization Methods and Algorithms
, M.Sc. Thesis Sharif University of Technology ; Joghataei, Abdolreza (Supervisor)
Abstract
In this thesis, the classical methods of structural optimization were studied and compared. After a comprehensive study on different methods of structural optimization, a reduced number of methods were selected in order to be used in this research. Exterior penalty function method, linear and quadratic extended penalty function methods were the selected constrained optimization methods. For unconstrained optimization, univariate method, Powell method, steepest descent method, Fletcher-Reeves method, and quasi-Newton methods of DFP and BFGS were used. And from different one-dimensional search methods studied, we used the golden section method. The structures which were optimally designed by...
Comparing Meta-Heuristic Algorithms for Solving Transportation Network Design Problem
, M.Sc. Thesis Sharif University of Technology ; Zokaei Aashtiani, Hedayat (Supervisor)
Abstract
Discrete network design problem is selecting a set of feasible projects among a set of candidate projects, considering a budget constraint in which user's cost is being minimized. Network design problem is one of the complicated problems in transportation science, since it is a bi-level problem in which the lower level is a traffic assignment problem and the upper level is finding the best set of arcs. Meta-heuristic algorithms can be used to solve this problem. Although, there is no guarantee to find the optimal solution by Meta-heuristic algorithms, but they find good solutions in short times. The purpose of this study is comparing the efficiency of three Meta-heuristic algorithms for...
Echelon Inventory Model with Fuzzy approach and Heuristic Solution for Supply Chain Management
, M.Sc. Thesis Sharif University of Technology ; Akbari Jokar, Mohammad Reza (Supervisor)
Abstract
This Project describes a new model for Inventory management in Supply Chain (SC), based on Fuzzy Set Theory and Echelon Inventory model. In today’s changing market, managing the Supply chain inventories, especially for the new product becomes more complicated and uncertain than before. This work provides a new approach to model uncertainties involved in supply chain management using fuzzy set theory. For achieving a more realistic model, Echelon Inventory is used as the base of integrated supply chain inventory model. In proposed model, some of input parameters like demand, production parameters, transportation time between inventories, and some of variables like lead times are described as...
Visibility Maintenance of a Moving Segment Observer Inside Polygons with Holes
, M.Sc. Thesis Sharif University of Technology ; Ghodsi, Mohammad (Supervisor) ; Safari, Mohammad Ali (Supervisor)Performance Evaluation of Recovery Based Routing Algorithms in Irregular Mesh NoCs
, M.Sc. Thesis Sharif University of Technology ; Sarbazi Azad, Hamid (Supervisor)
Abstract
Heterogeneity is one of the challenges in the current NoC (Network-on-Chip) domain which oblige designers to consider less regular topologies to provide the best cost-performance trade-off while minimizing resource and power consumption and providing the maximum flexibility. Irregular mesh is a topology which combines the benefits of regularity and advantage of irregularity. Another important issue in any NoC is the selection of routing algorithm which provides the best performance. Routing algorithms especially those coupled with wormhole switching should deal with deadlock occurrences. Deadlock detection and recovery-based routing schemes for this type of switching gained attraction since...
Exploiting Locality Properties of Nodes for Improving Search Efficiency in P2P Networks
, M.Sc. Thesis Sharif University of Technology ; Habibi, Jafar (Supervisor)
Abstract
The Use of peer-to-peer architectures instead of traditional client-server architecture can be beneficial in many aspects such as increasing scalability of the systems, enhancing fault tolerance in critical situations, extending the system resources and various other advantages. Nowadays, many applications are based on peer-to-peer architectures and as a result, a large portion of the internet traffic is produced by these applications. This has been a motivation to many researchers to focus on reducing the amount of this traffic while satisfying the content distribution demands. One of the main problems that can result in generating large amount of traffic and also long response times for...
Software Test Data Generation Using Genetic Algorithms
, M.Sc. Thesis Sharif University of Technology ; Mahdavi Amiri, Nezameddin (Supervisor)
Abstract
In software testing, it is often desirable to find test inputs that exercise specific program features. Good testing means uncovering as many faults as possible with a potent set of tests. Thus, a test series that has the potential to uncover many faults is better than one that can only uncover a few. To find these inputs by hand is extremely time-consuming, especially when the software is complex. Therefore, many attempts have been made to automate the process. There are three major methods to generate software test data: Random test data generation, Symbolic test data generation and Dynamic test data generation. Dynamic test data generation, such as those using genetic algorithms, is...