Solving the Vehicle Routing Problem with Genetic ALgorithm and Simulated Annealing

This Thesis Work will concentrate on a very interesting problem, the Vehicle Routing Problem (VRP). In this problem, customers or cities have to be visited and packages have to be transported to each of them, starting from a basis point on the map. The goal is to solve the transportation problem, to be able to deliver the packages-on time for the customers,-enough package for each Customer,-using the available resources- and – of course - to be so effective as it is possible.Although this problem seems to be very easy to solve with a small number of cities or customers, it is not. In this problem the algorithm have to face with several constraints, for example opening hours, package delivery times, truck capacities, etc. This makes this problem a so called Multi Constraint Optimization Problem (MCOP). What’s more, this problem is intractable with current amount of computational power which is available for most of us. As the number of customers grow, the calculations to be done grows exponential fast, because all constraints have to be solved for each customers and it should not be forgotten that the goal is to find a solution, what is best enough, before the time for the calculation is up. This problem is introduced in the first chapter: form its basics, the Traveling Salesman Problem, using some theoretical and mathematical background it is shown, why is it so hard to optimize this problem, and although it is so hard, and there is no best algorithm known for huge number of customers, why is it a worth to deal with it. Just think about a huge transportation company with ten thousands of trucks, millions of customers: how much money could be saved if we would know the optimal path for all our packages.Although there is no best algorithm is known for this kind of optimization problems, we are trying to give an acceptable solution for it in the second and third chapter, where two algorithms are described: the Genetic Algorithm and the Simulated Annealing. Both of them are based on obtaining the processes of nature and material science. These algorithms will hardly ever be able to find the best solution for the problem, but they are able to give a very good solution in special cases within acceptable calculation time.In these chapters (2nd and 3rd) the Genetic Algorithm and Simulated Annealing is described in details, from their basis in the “real world” through their terminology and finally the basic implementation of them. The work will put a stress on the limits of these algorithms, their advantages and disadvantages, and also the comparison of them to each other.Finally, after all of these theories are shown, a simulation will be executed on an artificial environment of the VRP, with both Simulated Annealing and Genetic Algorithm. They will both solve the same problem in the same environment and are going to be compared to each other. The environment and the implementation are also described here, so as the test results obtained.Finally the possible improvements of these algorithms are discussed, and the work will try to answer the “big” question, “Which algorithm is better?”, if this question even exists.

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Knowledge based crime scenario modelling

A crucial concern in the evaluation of evidence related to a major crime is the formulation of sufficient alternative plausible scenarios that can explain the available evidence. However, software aimed at assisting human crime investigators by automatically constructing crime scenarios from evidence is difficult to develop because of the almost infinite variation of plausible crime scenarios. This paper introduces a novel knowledge driven methodology for crime scenario construction and it presents a decision support system based on it. The approach works by storing the component events of the scenarios instead of entire scenarios and by providing an algorithm that can instantiate and compose these component events into useful scenarios. The scenario composition approach is highly adaptable to unanticipated cases because it allows component events to match the case under investigation in many different ways. Given a description of the available evidence, it generates a network of plausible scenarios that can then be analysed to devise effective evidence collection strategies. The applicability of the ideas presented here are demonstrated by means of a realistic example and prototype decision support software.

Evidence Algorithm and Processing Formalized Mathematical Texts

The goal of a research programme Evidence Algorithm is a development of an open system of automated proving that is able to accumulate mathematical knowledge and to prove theorems in a context of a self-contained mathematical text. By now, the first version of such a system called a System for Automated Deduction, SAD, is implemented in software. The system SAD possesses the following main features: mathematical texts are formalized using a specific formal language that is close to a natural language of mathematical publications; a proof search is based on special sequent-type calculi formalizing natural reasoning style, such as application of definitions and auxiliary propositions. These calculi also admit a separation of equality handling from deduction that gives an opportunity to integrate logical reasoning with symbolic calculation.

Optimization Of Groundwater Remediation With New Efficient Parallel Algorithm For Global Optimization

Application of optimization algorithm to PDE modeling groundwater remediation can greatly reduce remediation cost. However, groundwater remediation analysis requires a computational expensive simulation, therefore, effective parallel optimization could potentially greatly reduce computational expense. The optimization algorithm used in this research is Parallel Stochastic radial basis function. This is designed for global optimization of computationally expensive functions with multiple local optima and it does not require derivatives. In each iteration of the algorithm, an RBF is updated based on all the evaluated points in order to approximate expensive function. Then the new RBF surface is used to generate the next set of points, which will be distributed to multiple processors for evaluation. The criteria of selection of next function evaluation points are estimated function value and distance from all the points known. Algorithms created for serial computing are not necessarily efficient in parallel so Parallel Stochastic RBF is different algorithm from its serial ancestor. The application for two Groundwater Superfund Remediation sites, Umatilla Chemical Depot, and Former Blaine Naval Ammunition Depot. In the study, the formulation adopted treats pumping rates as decision variables in order to remove plume of contaminated groundwater. Groundwater flow and contamination transport is simulated with MODFLOW-MT3DMS. For both problems, computation takes a large amount of CPU time, especially for Blaine problem, which requires nearly fifty minutes for a simulation for a single set of decision variables. Thus, efficient algorithm and powerful computing resource are essential in both cases. The results are discussed in terms of parallel computing metrics i.e. speedup and efficiency. We find that with use of up to 24 parallel processors, the results of the parallel Stochastic RBF algorithm are excellent with speed up efficiencies close to or exceeding 100%.

Multi-Objective Pipe Smoothing Genetic Algorithm For Water Distribution Network Design

This paper describes the formulation of a Multi-objective Pipe Smoothing Genetic Algorithm (MOPSGA) and its application to the least cost water distribution network design problem. Evolutionary Algorithms have been widely utilised for the optimisation of both theoretical and real-world non-linear optimisation problems, including water system design and maintenance problems. In this work we present a pipe smoothing based approach to the creation and mutation of chromosomes which utilises engineering expertise with the view to increasing the performance of the algorithm whilst promoting engineering feasibility within the population of solutions. MOPSGA is based upon the standard Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and incorporates a modified population initialiser and mutation operator which directly targets elements of a network with the aim to increase network smoothness (in terms of progression from one diameter to the next) using network element awareness and an elementary heuristic. The pipe smoothing heuristic used in this algorithm is based upon a fundamental principle employed by water system engineers when designing water distribution pipe networks where the diameter of any pipe is never greater than the sum of the diameters of the pipes directly upstream resulting in the transition from large to small diameters from source to the extremities of the network. MOPSGA is assessed on a number of water distribution network benchmarks from the literature including some real-world based, large scale systems. The performance of MOPSGA is directly compared to that of NSGA-II with regard to solution quality, engineering feasibility (network smoothness) and computational efficiency. MOPSGA is shown to promote both engineering and hydraulic feasibility whilst attaining good infrastructure costs compared to NSGA-II.

Study of the audio coding algorithm of the MPEG-4 AAC standard and comparison among implementations of modules of the algorithm

Audio coding is used to compress digital audio signals, thereby reducing the amount of bits needed to transmit or to store an audio signal. This is useful when network bandwidth or storage capacity is very limited. Audio compression algorithms are based on an encoding and decoding process. In the encoding step, the uncompressed audio signal is transformed into a coded representation, thereby compressing the audio signal. Thereafter, the coded audio signal eventually needs to be restored (e.g. for playing back) through decoding of the coded audio signal. The decoder receives the bitstream and reconverts it into an uncompressed signal. ISO-MPEG is a standard for high-quality, low bit-rate video and audio coding. The audio part of the standard is composed by algorithms for high-quality low-bit-rate audio coding, i.e. algorithms that reduce the original bit-rate, while guaranteeing high quality of the audio signal. The audio coding algorithms consists of MPEG-1 (with three different layers), MPEG-2, MPEG-2 AAC, and MPEG-4. This work presents a study of the MPEG-4 AAC audio coding algorithm. Besides, it presents the implementation of the AAC algorithm on different platforms, and comparisons among implementations. The implementations are in C language, in Assembly of Intel Pentium, in C-language using DSP processor, and in HDL. Since each implementation has its own application niche, each one is valid as a final solution. Moreover, another purpose of this work is the comparison among these implementations, considering estimated costs, execution time, and advantages and disadvantages of each one.

Reuse-based test planning for core-based systems-on-chip

Electronic applications are currently developed under the reuse-based paradigm. This design methodology presents several advantages for the reduction of the design complexity, but brings new challenges for the test of the final circuit. The access to embedded cores, the integration of several test methods, and the optimization of the several cost factors are just a few of the several problems that need to be tackled during test planning. Within this context, this thesis proposes two test planning approaches that aim at reducing the test costs of a core-based system by means of hardware reuse and integration of the test planning into the design flow. The first approach considers systems whose cores are connected directly or through a functional bus. The test planning method consists of a comprehensive model that includes the definition of a multi-mode access mechanism inside the chip and a search algorithm for the exploration of the design space. The access mechanism model considers the reuse of functional connections as well as partial test buses, cores transparency, and other bypass modes. The test schedule is defined in conjunction with the access mechanism so that good trade-offs among the costs of pins, area, and test time can be sought. Furthermore, system power constraints are also considered. This expansion of concerns makes it possible an efficient, yet fine-grained search, in the huge design space of a reuse-based environment. Experimental results clearly show the variety of trade-offs that can be explored using the proposed model, and its effectiveness on optimizing the system test plan. Networks-on-chip are likely to become the main communication platform of systemson- chip. Thus, the second approach presented in this work proposes the reuse of the on-chip network for the test of the cores embedded into the systems that use this communication platform. A power-aware test scheduling algorithm aiming at exploiting the network characteristics to minimize the system test time is presented. The reuse strategy is evaluated considering a number of system configurations, such as different positions of the cores in the network, power consumption constraints and number of interfaces with the tester. Experimental results show that the parallelization capability of the network can be exploited to reduce the system test time, whereas area and pin overhead are strongly minimized. In this manuscript, the main problems of the test of core-based systems are firstly identified and the current solutions are discussed. The problems being tackled by this thesis are then listed and the test planning approaches are detailed. Both test planning techniques are validated for the recently released ITC’02 SoC Test Benchmarks, and further compared to other test planning methods of the literature. This comparison confirms the efficiency of the proposed methods.

Multistep stochastic mirror descent for risk-averse convex stochastic programs based on extended polyhedral risk measures

We consider risk-averse convex stochastic programs expressed in terms of extended polyhedral risk measures. We derive computable con dence intervals on the optimal value of such stochastic programs using the Robust Stochastic Approximation and the Stochastic Mirror Descent (SMD) algorithms. When the objective functions are uniformly convex, we also propose a multistep extension of the Stochastic Mirror Descent algorithm and obtain con dence intervals on both the optimal values and optimal solutions. Numerical simulations show that our con dence intervals are much less conservative and are quicker to compute than previously obtained con dence intervals for SMD and that the multistep Stochastic Mirror Descent algorithm can obtain a good approximate solution much quicker than its nonmultistep counterpart. Our con dence intervals are also more reliable than asymptotic con dence intervals when the sample size is not much larger than the problem size.

Contagion in financial networks: a network theory and agent-based approaches to modeling the spread of risk in financial systems

Esta dissertação estuda a propagação de crises sobre o sistema financeiro. Mais especi- ficamente, busca-se desenvolver modelos que permitam simular como um determinado choque econômico atinge determinados agentes do sistema financeiro e apartir dele se propagam, transformando-se em um problema sistêmico. A dissertação é dividida em dois capítulos,além da introdução. O primeiro capítulo desenvolve um modelo de propa- gação de crises em fundos de investimento baseado em ciência das redes.Combinando dois modelos de propagação em redes financeiras, um simulando a propagação de perdas em redes bipartites de ativos e agentes financeiros e o outro simulando a propagação de perdas em uma rede de investimentos diretos em quotas de outros agentes, desenvolve-se um algoritmo para simular a propagação de perdas através de ambos os mecanismos e utiliza-se este algoritmo para simular uma crise no mercado brasileiro de fundos de investimento. No capítulo 2,desenvolve-se um modelo de simulação baseado em agentes, com agentes financeiros, para simular propagação de um choque que afeta o mercado de operações compromissadas.Criamos também um mercado artificial composto por bancos, hedge funds e fundos de curto prazo e simulamos a propagação de um choque de liquidez sobre um ativo de risco securitizando utilizado para colateralizar operações compromissadas dos bancos.

New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM)

LEÃO, Adriano de Castro; DÓRIA NETO, Adrião Duarte; SOUSA, Maria Bernardete Cordeiro de. New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM). Computers in Biology and Medicine, v. 39, p. 853-859, 2009

Localization of a mobile robot based on odometry and natural landmarks using extended kalman filter

SANTANA, André M.; SOUZA, Anderson A. S.; BRITTO, Ricardo S.; ALSINA, Pablo J.; MEDEIROS, Adelardo A. D. Localization of a mobile robot based on odometry and natural landmarks using extended Kalman Filter. In: INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, 5., 2008, Funchal, Portugal. Proceedings... Funchal, Portugal: ICINCO, 2008.

Parallel Cyclostationarity-Exploiting Algorithm for Energy-Efficient Spectrum Sensing

The evolution of wireless communication systems leads to Dynamic Spectrum Allocation for Cognitive Radio, which requires reliable spectrum sensing techniques. Among the spectrum sensing methods proposed in the literature, those that exploit cyclostationary characteristics of radio signals are particularly suitable for communication environments with low signal-to-noise ratios, or with non-stationary noise. However, such methods have high computational complexity that directly raises the power consumption of devices which often have very stringent low-power requirements. We propose a strategy for cyclostationary spectrum sensing with reduced energy consumption. This strategy is based on the principle that p processors working at slower frequencies consume less power than a single processor for the same execution time. We devise a strict relation between the energy savings and common parallel system metrics. The results of simulations show that our strategy promises very significant savings in actual devices.

H-infinity control design for time-delay linear systems: a rational transfer function based approach

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Evaluation of the adequacy of diflubenzuron and dechlorane in toxic baits for leaf-cutting ants (Hymenoptera : Formicidae) based on formicidal activity

Studies assessing the characteristics of active ingredients (AIs) of toxic baits for leaf-cutting ants are still scarce, although the need for a delayed action of these compounds on adult workers has been well accepted (mortality <= 15% at 24 h and <= 90% at 21 days). Therefore, we determined the insecticidal action of two AIs used in commercial baits, diflubenzuron and dechlorane, over time in workers, and discussed the control of colonies in relation to the existing literature. Dechlorane presented excellent insecticidal activity with a delayed action at all concentrations tested, although its commercial use has been prohibited due to its organochlorine nature. In contrast, diflubenzuron did not cause significant mortality or symptoms of intoxication, indicating that the successful cases reported with the use of baits containing this AI were in fact due to an accidental contamination with dechlorane. We comment about the ineffectiveness of diflubenzuron on alternative targets, i.e., young forms and the mutualistic fungus, supporting the concept that the AI needs to a have a delayed action on adult workers.