Algoritmo memético com infecção viral: uma aplicação ao problema do caixeiro viajante assimétrico

The Combinatorial Optimization is a basic area to companies who look for competitive advantages in the diverse productive sectors and the Assimetric Travelling Salesman Problem, which one classifies as one of the most important problems of this area, for being a problem of the NP-hard class and for possessing diverse practical applications, has increased interest of researchers in the development of metaheuristics each more efficient to assist in its resolution, as it is the case of Memetic Algorithms, which is a evolutionary algorithms that it is used of the genetic operation in combination with a local search procedure. This work explores the technique of Viral Infection in one Memetic Algorithms where the infection substitutes the mutation operator for obtaining a fast evolution or extinguishing of species (KANOH et al, 1996) providing a form of acceleration and improvement of the solution . For this it developed four variants of Viral Infection applied in the Memetic Algorithms for resolution of the Assimetric Travelling Salesman Problem where the agent and the virus pass for a symbiosis process which favored the attainment of a hybrid evolutionary algorithms and computational viable

Aplicaçaõ das técnicas Path-relinking e Vocabulary buiding na melhoria de performance do algoritmo memético para o problema do caixeiro viajante assimétrico

The present essay shows strategies of improvement in a well succeded evolutionary metaheuristic to solve the Asymmetric Traveling Salesman Problem. Such steps consist in a Memetic Algorithm projected mainly to this problem. Basically this improvement applied optimizing techniques known as Path-Relinking and Vocabulary Building. Furthermore, this last one has being used in two different ways, in order to evaluate the effects of the improvement on the evolutionary metaheuristic. These methods were implemented in C++ code and the experiments were done under instances at TSPLIB library, being possible to observe that the procedures purposed reached success on the tests done

Busca heurística através de algoritmo genético e memético com construção de vocábulos para o problema de atribuição de localidades a anéis Sonet

Telecommunications play a key role in contemporary society. However, as new technologies are put into the market, it also grows the demanding for new products and services that depend on the offered infrastructure, making the problems of planning telecommunications networks, despite the advances in technology, increasingly larger and complex. However, many of these problems can be formulated as models of combinatorial optimization, and the use of heuristic algorithms can help solving these issues in the planning phase. In this project it was developed two pure metaheuristic implementations Genetic algorithm (GA) and Memetic Algorithm (MA) plus a third hybrid implementation Memetic Algorithm with Vocabulary Building (MA+VB) for a problem in telecommunications that is known in the literature as Problem SONET Ring Assignment Problem or SRAP. The SRAP arises during the planning stage of the physical network and it consists in the selection of connections between a number of locations (customers) in order to meet a series of restrictions on the lowest possible cost. This problem is NP-hard, so efficient exact algorithms (in polynomial complexity ) are not known and may, indeed, even exist

Um algoritmo híbrido para o problema de roteamento de veículos com frotas heterogêneas

This paper aims to propose a hybrid meta-heuristics for the Heterogeneous Fleet Vehicle Routing Problem (HVRP), which is a combinatorial optimization problem NP-hard, and is characterized by the use of a limited fleet consists of different vehicles with different capacities. The hybrid method developed makes use of a memetic algorithm associated with the component optimizer Vocabulary Building. The resulting hybrid meta-heuristic was implemented in the programming language C + + and computational experiments generated good results in relation to meta-heuristic applied in isolation, proving the efficiency of the proposed method.

O problema do caixeiro viajante alugador : um estudo algorítmico

The Car Rental Salesman Problem (CaRS) is a variant of the classical Traveling Salesman Problem which was not described in the literature where a tour of visits can be decomposed into contiguous paths that may be performed in different rental cars. The aim is to determine the Hamiltonian cycle that results in a final minimum cost, considering the cost of the route added to the cost of an expected penalty paid for each exchange of vehicles on the route. This penalty is due to the return of the car dropped to the base. This paper introduces the general problem and illustrates some examples, also featuring some of its associated variants. An overview of the complexity of this combinatorial problem is also outlined, to justify their classification in the NPhard class. A database of instances for the problem is presented, describing the methodology of its constitution. The presented problem is also the subject of a study based on experimental algorithmic implementation of six metaheuristic solutions, representing adaptations of the best of state-of-the-art heuristic programming. New neighborhoods, construction procedures, search operators, evolutionary agents, cooperation by multi-pheromone are created for this problem. Furtermore, computational experiments and comparative performance tests are conducted on a sample of 60 instances of the created database, aiming to offer a algorithm with an efficient solution for this problem. These results will illustrate the best performance reached by the transgenetic algorithm in all instances of the dataset

Uma análise experimental de abordagens heurísticas aplicadas ao problema do caixeiro viajante

Due to great difficulty of accurate solution of Combinatorial Optimization Problems, some heuristic methods have been developed and during many years, the analysis of performance of these approaches was not carried through in a systematic way. The proposal of this work is to make a statistical analysis of heuristic approaches to the Traveling Salesman Problem (TSP). The focus of the analysis is to evaluate the performance of each approach in relation to the necessary computational time until the attainment of the optimal solution for one determined instance of the TSP. Survival Analysis, assisted by methods for the hypothesis test of the equality between survival functions was used. The evaluated approaches were divided in three classes: Lin-Kernighan Algorithms, Evolutionary Algorithms and Particle Swarm Optimization. Beyond those approaches, it was enclosed in the analysis, a memetic algorithm (for symmetric and asymmetric TSP instances) that utilizes the Lin-Kernighan heuristics as its local search procedure

Um estudo algorítmico da programação da intervenção de sondas de produção

This work approaches the Scheduling Workover Rigs Problem (SWRP) to maintain the wells of an oil field, although difficult to resolve, is extremely important economical, technical and environmental. A mathematical formulation of this problem is presented, where an algorithmic approach was developed. The problem can be considered to find the best scheduling service to the wells by the workover rigs, taking into account the minimization of the composition related to the costs of the workover rigs and the total loss of oil suffered by the wells. This problem is similar to the Vehicle Routing Problem (VRP), which is classified as belonging to the NP-hard class. The goal of this research is to develop an algorithmic approach to solve the SWRP, using the fundamentals of metaheuristics like Memetic Algorithm and GRASP. Instances are generated for the tests to analyze the computational performance of the approaches mentioned above, using data that are close to reality. Thereafter, is performed a comparison of performance and quality of the results obtained by each one of techniques used

O problema do caixeiro alugador com coleta de bonus: um estudo algoritmico

This paper introduces a new variant of the Traveling Car Renter Problem, named Prizecollecting Traveling Car Renter Problem. In this problem, a set of vertices, each associated with a bonus, and a set of vehicles are given. The objective is to determine a cycle that visits some vertices collecting, at least, a pre-defined bonus, and minimizing the cost of the tour that can be traveled with different vehicles. A mathematical formulation is presented and implemented in a solver to produce results for sixty-two instances. The proposed problem is also subject of an experimental study based on the algorithmic application of four metaheuristics representing the best adaptations of the state of the art of the heuristic programming.We also provide new local search operators which exploit the neighborhoods of the problem, construction procedures and adjustments, created specifically for the addressed problem. Comparative computational experiments and performance tests are performed on a sample of 80 instances, aiming to offer a competitive algorithm to the problem. We conclude that memetic algorithms, computational transgenetic and a hybrid evolutive algorithm are competitive in tests performed

Implementação do algoritmo (RTM) para processamento sísmico em arquiteturas não convencionais

With the growth of energy consumption worldwide, conventional reservoirs, the reservoirs called "easy exploration and production" are not meeting the global energy demand. This has led many researchers to develop projects that will address these needs, companies in the oil sector has invested in techniques that helping in locating and drilling wells. One of the techniques employed in oil exploration process is the reverse time migration (RTM), in English, Reverse Time Migration, which is a method of seismic imaging that produces excellent image of the subsurface. It is algorithm based in calculation on the wave equation. RTM is considered one of the most advanced seismic imaging techniques. The economic value of the oil reserves that require RTM to be localized is very high, this means that the development of these algorithms becomes a competitive differentiator for companies seismic processing. But, it requires great computational power, that it still somehow harms its practical success. The objective of this work is to explore the implementation of this algorithm in unconventional architectures, specifically GPUs using the CUDA by making an analysis of the difficulties in developing the same, as well as the performance of the algorithm in the sequential and parallel version

Algoritmo evolutivo paralelo para o problema de atribuição de localidades a anéis em redes sonet/sdh

The telecommunications play a fundamental role in the contemporary society, having as one of its main roles to give people the possibility to connect them and integrate them into society in which they operate and, therewith, accelerate development through knowledge. But as new technologies are introduced on the market, increases the demand for new products and services that depend on the infrastructure offered, making the problems of planning of telecommunication networks become increasingly large and complex. Many of these problems, however, can be formulated as combinatorial optimization models, and the use of heuristic algorithms can help solve these issues in the planning phase. This paper proposes the development of a Parallel Evolutionary Algorithm to be applied to telecommunications problem known in the literature as SONET Ring Assignment Problem SRAP. This problem is the class NP-hard and arises during the physical planning of a telecommunication network and consists of determining the connections between locations (customers), satisfying a series of constrains of the lowest possible cost. Experimental results illustrate the effectiveness of the Evolutionary Algorithm parallel, over other methods, to obtain solutions that are either optimal or very close to it

Algoritmo Q-learning como estratégia de exploração e/ou explotação para metaheurísticas GRASP e algoritmo genético

Techniques of optimization known as metaheuristics have achieved success in the resolution of many problems classified as NP-Hard. These methods use non deterministic approaches that reach very good solutions which, however, don t guarantee the determination of the global optimum. Beyond the inherent difficulties related to the complexity that characterizes the optimization problems, the metaheuristics still face the dilemma of xploration/exploitation, which consists of choosing between a greedy search and a wider exploration of the solution space. A way to guide such algorithms during the searching of better solutions is supplying them with more knowledge of the problem through the use of a intelligent agent, able to recognize promising regions and also identify when they should diversify the direction of the search. This way, this work proposes the use of Reinforcement Learning technique - Q-learning Algorithm - as exploration/exploitation strategy for the metaheuristics GRASP (Greedy Randomized Adaptive Search Procedure) and Genetic Algorithm. The GRASP metaheuristic uses Q-learning instead of the traditional greedy-random algorithm in the construction phase. This replacement has the purpose of improving the quality of the initial solutions that are used in the local search phase of the GRASP, and also provides for the metaheuristic an adaptive memory mechanism that allows the reuse of good previous decisions and also avoids the repetition of bad decisions. In the Genetic Algorithm, the Q-learning algorithm was used to generate an initial population of high fitness, and after a determined number of generations, where the rate of diversity of the population is less than a certain limit L, it also was applied to supply one of the parents to be used in the genetic crossover operator. Another significant change in the hybrid genetic algorithm is the proposal of a mutually interactive cooperation process between the genetic operators and the Q-learning algorithm. In this interactive/cooperative process, the Q-learning algorithm receives an additional update in the matrix of Q-values based on the current best solution of the Genetic Algorithm. The computational experiments presented in this thesis compares the results obtained with the implementation of traditional versions of GRASP metaheuristic and Genetic Algorithm, with those obtained using the proposed hybrid methods. Both algorithms had been applied successfully to the symmetrical Traveling Salesman Problem, which was modeled as a Markov decision process

Um algoritmo para estimação de estado em alimentadores de distribuição de energia elétrica com base no método da soma de potências

Most algorithms for state estimation based on the classical model are just adequate for use in transmission networks. Few algorithms were developed specifically for distribution systems, probably because of the little amount of data available in real time. Most overhead feeders possess just current and voltage measurements at the middle voltage bus-bar at the substation. In this way, classical algorithms are of difficult implementation, even considering off-line acquired data as pseudo-measurements. However, the necessity of automating the operation of distribution networks, mainly in regard to the selectivity of protection systems, as well to implement possibilities of load transfer maneuvers, is changing the network planning policy. In this way, some equipments incorporating telemetry and command modules have been installed in order to improve operational features, and so increasing the amount of measurement data available in real-time in the System Operation Center (SOC). This encourages the development of a state estimator model, involving real-time information and pseudo-measurements of loads, that are built from typical power factors and utilization factors (demand factors) of distribution transformers. This work reports about the development of a new state estimation method, specific for radial distribution systems. The main algorithm of the method is based on the power summation load flow. The estimation is carried out piecewise, section by section of the feeder, going from the substation to the terminal nodes. For each section, a measurement model is built, resulting in a nonlinear overdetermined equations set, whose solution is achieved by the Gaussian normal equation. The estimated variables of a section are used as pseudo-measurements for the next section. In general, a measurement set for a generic section consists of pseudo-measurements of power flows and nodal voltages obtained from the previous section or measurements in real-time, if they exist -, besides pseudomeasurements of injected powers for the power summations, whose functions are the load flow equations, assuming that the network can be represented by its single-phase equivalent. The great advantage of the algorithm is its simplicity and low computational effort. Moreover, the algorithm is very efficient, in regard to the accuracy of the estimated values. Besides the power summation state estimator, this work shows how other algorithms could be adapted to provide state estimation of middle voltage substations and networks, namely Schweppes method and an algorithm based on current proportionality, that is usually adopted for network planning tasks. Both estimators were implemented not only as alternatives for the proposed method, but also looking for getting results that give support for its validation. Once in most cases no power measurement is performed at beginning of the feeder and this is required for implementing the power summation estimations method, a new algorithm for estimating the network variables at the middle voltage bus-bar was also developed

Análise comparativa do desempenho de um Controlador Fuzzy acoplado a um PID Neural sintonizado por um Algoritmo Genético com Controladores Inteligentes Convencionais

On this paper, it is made a comparative analysis among a controller fuzzy coupled to a PID neural adjusted by an AGwith several traditional control techniques, all of them applied in a system of tanks (I model of 2nd order non lineal). With the objective of making possible the techniques involved in the comparative analysis and to validate the control to be compared, simulations were accomplished of some control techniques (conventional PID adjusted by GA, Neural PID (PIDN) adjusted by GA, Fuzzy PI, two Fuzzy attached to a PID Neural adjusted by GA and Fuzzy MISO (3 inputs) attached to a PIDN adjusted by GA) to have some comparative effects with the considered controller. After doing, all the tests, some control structures were elected from all the tested techniques on the simulating stage (conventional PID adjusted by GA, Fuzzy PI, two Fuzzy attached to a PIDN adjusted by GA and Fuzzy MISO (3 inputs) attached to a PIDN adjusted by GA), to be implemented at the real system of tanks. These two kinds of operation, both the simulated and the real, were very important to achieve a solid basement in order to establish the comparisons and the possible validations show by the results

Implementação de um algoritmo para a análise de curtos-circuitos de alta impedância baseado no fluxo de carga soma de potências

This work has as main objective to show all the particularities regarding the Three-phase Power Summation Method, used for load flow calculation, in what it says respect to the influence of the magnetic coupling among the phases, as well as to the losses presented in all the existent transformers in the feeder to be analyzed. Besides, its application is detailed in the study of the short-circuits, that happen in the presence of high impedance values, which possess a problem, that is its difficult detection and consequent elimination on the part of common devices of protection. That happens due to the characteristic presented by the current of short¬ circuit, in being generally of the same order of greatness that the load currents. Results of simulations accomplished in several situations will be shown, objectifying a complete analysis of the behavior of the proposed method in several types of short-circuits. Confront of the results obtained by the method with results of another works will be presented to verify its effectiveness

Algoritmo SOM com estrutura hierárquica e dinâmica aplicado a compressão de imagens

ln this work the implementation of the SOM (Self Organizing Maps) algorithm or Kohonen neural network is presented in the form of hierarchical structures, applied to the compression of images. The main objective of this approach is to develop an Hierarchical SOM algorithm with static structure and another one with dynamic structure to generate codebooks (books of codes) in the process of the image Vector Quantization (VQ), reducing the time of processing and obtaining a good rate of compression of images with a minimum degradation of the quality in relation to the original image. Both self-organizing neural networks developed here, were denominated HSOM, for static case, and DHSOM, for the dynamic case. ln the first form, the hierarchical structure is previously defined and in the later this structure grows in an automatic way in agreement with heuristic rules that explore the data of the training group without use of external parameters. For the network, the heuristic mIes determine the dynamics of growth, the pruning of ramifications criteria, the flexibility and the size of children maps. The LBO (Linde-Buzo-Oray) algorithm or K-means, one ofthe more used algorithms to develop codebook for Vector Quantization, was used together with the algorithm of Kohonen in its basic form, that is, not hierarchical, as a reference to compare the performance of the algorithms here proposed. A performance analysis between the two hierarchical structures is also accomplished in this work. The efficiency of the proposed processing is verified by the reduction in the complexity computational compared to the traditional algorithms, as well as, through the quantitative analysis of the images reconstructed in function of the parameters: (PSNR) peak signal-to-noise ratio and (MSE) medium squared error