Comparison of Feedforward and Feedback Neural Network Architectures for Short Term Wind Speed Prediction

This paper compares three types of neural networks trained using particle swarm optimization (PSO) for use in the short term prediction of wind speed. The three types of neural networks compared are the multi-layer perceptron (MLP) neural network, Elman recurrent neural network, and simultaneous recurrent neural network (SRN). Each network is trained and tested using meteorological data of one week measured at the National Renewable Energy Laboratory National Wind Technology Center near Boulder, CO. Results show that while the recurrent neural networks outperform the MLP in the best and average case with a lower overall mean squared error, the MLP performance is comparable. The better performance of the feedback architectures is also shown using the mean absolute relative error. While the SRN performance is superior, the increase in required training time for the SRN over the other networks may be a constraint, depending on the application

A Genetic Algorithm Based Approach for Solving the Minimum Dominating Set of Queens Problem

In the field of computing, combinatorics, and related areas, researchers have formulated several techniques for the Minimum Dominating Set of Queens Problem (MDSQP) pertaining to the typical chessboard based puzzles. However, literature shows that limited research has been carried out to solve theMDSQP using bioinspired algorithms. To fill this gap, this paper proposes a simple and effective solution based on genetic algorithms to solve this classical problem. We report results which demonstrate that near optimal solutions have been determined by the GA for different board sizes ranging from 8 × 8 to 11 × 11

A Comparative Study of Single-Constraint Routing in Wireless Mesh Networks Using Different Dynamic Programming Algorithms

Finding the shortest route in wireless mesh networks is an important aspect. Many techniques are used to solve this problem like dynamic programming, evolutionary algorithms, weighted-sum techniques, and others. In this paper, we use dynamic programming techniques to find the shortest path in wireless mesh networks due to their generality, reduction of complexity and facilitation of numerical computation, simplicity in incorporating constraints, and their onformity to the stochastic nature of some problems. The routing problem is a multi-objective optimization problem with some constraints such as path capacity and end-to-end delay. Single-constraint routing problems and solutions using Dijkstra, Bellman-Ford, and Floyd-Warshall algorithms are proposed in this work with a discussion on the difference between them. These algorithms find the shortest route through finding the optimal rate between two nodes in the wireless networks but with bounded end-to-end delay. The Dijkstra-based algorithm is especially favorable in terms of processing time. We also present a comparison between our proposed single-constraint Dijkstra-based routing algorithm and the mesh routing algorithm (MRA) existing in the literature to clarify the merits of the former

Parameterized Complexity Analysis of Randomized Search Heuristics

This chapter compiles a number of results that apply the theory of parameterized algorithmics to the running-time analysis of randomized search heuristics such as evolutionary algorithms. The parameterized approach articulates the running time of algorithms solving combinatorial problems in finer detail than traditional approaches from classical complexity theory. We outline the main results and proof techniques for a collection of randomized search heuristics tasked to solve NP-hard combinatorial optimization problems such as finding a minimum vertex cover in a graph, finding a maximum leaf spanning tree in a graph, and the traveling salesperson problem.Comment: This is a preliminary version of a chapter in the book "Theory of Evolutionary Computation: Recent Developments in Discrete Optimization", edited by Benjamin Doerr and Frank Neumann, published by Springe

Development of a fuel-saving algorithm for a vehicle's driver assistant system

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Mecânica, Florianópolis, 2018.A fim de reduzir o consumo de combustível em sistemas de propulsão automotivos, a implementação de conjuntos motrizes híbridos, o downsizing de motores à combustão interna e a automatização do câmbio têm crescido no mercado de veículos de passeio. No entanto, as melhorias individuais em sistemas de um veículo não necessariamente aproximam a sua operação do ponto de ótima eficiência, e a adição de diferentes fontes de energia deve ser feita de forma metódica e estruturada, a fim de proporcionar ganhos consideráveis em consumo de combustível. Ademais, o comportamento do condutor e as trajetórias percorridas pelo veículo são características extremamente dependentes da região em análise, dificultando ainda mais o desenvolvimento de uma estratégia única de redução de consumo de combustível. Assim, a partir de um modelo de dinâmica longitudinal com três graus de liberdade para um veículo genérico, desenvolvido utilizando as equações de Euler-Lagrange do segundo tipo, essa dissertação tem como objetivo principal a proposta de um algoritmo para um assistente de direção automotivo, o qual promove a redução do consumo de combustível a partir do ajuste da relação de transmissão e abertura da válvula borboleta, em função da demanda de torque imposta pelo condutor, dinâmica do powertrain e características da fonte de potência. As características de desempenho do motor foram modeladas utilizando Redes Neurais Artificiais do tipo Feedforward Multi-Layer Perceptron, viabilizando a simulação de ciclos urbanos em tempo hábil e a inserção de propriedades relacionadas ao gradiente dos mapas estáticos no algoritmo do assistente de direção. O sistema foi implementado e simulado em Matlab , e seu desempenho avaliado através de um estudo de caso, utilizando modelos da literatura como referência.Abstract : The adoption of hybrid powertrain systems in passenger vehicles, as well as downsized engines and automatic transmissions, has been increasing in the last years as solutions to reduce the fuel consumption. However, the individual optimization of components or layout does not necessarily approximates the operation to conditions of maximum efficiency, and the addition of power sources should be done methodically, such that improvements of fuel efficiency can actually be achieved. Furthermore, the behavior of the driver and traffic conditions, factors which have major influence on the fuel consumption, vary with the geographic region, increasing the difficulty to develop a single solution to minimize the fuel consumption. Given such complex scenario, this dissertation proposes an algorithm for a Fuel-saving Driver Assistant System, which actuates on the throttle valve and gearbox, based on the demand of torque imposed by the driver, powertrain dynamics and characteristics of the power sources. In order to do so, a mathematical model of powertrain and longitudinal dynamics with 3 Degrees of Freedom was developed, which allows the simulation of urban traffic conditions. The performance of the engine was modeled using Artificial Neural Networks (ANN), which allies a flexible representation of the nonlinear characteristics of the power source, low computational costs and possibility to derive gradient information from the static maps, which is used by the Driver Assistant Algorithm. The system was implemented on Matlab and its performance compared to different models available in the literature

Random combinatorial structures and randomized search heuristics

This thesis is concerned with the probabilistic analysis of random combinatorial structures and the runtime analysis of randomized search heuristics. On the subject of random structures, we investigate two classes of combinatorial objects. The first is the class of planar maps and the second is the class of generalized parking functions. We identify typical properties of these structures and show strong concentration results on the probabilities that these properties hold. To this end, we develop and apply techniques based on exact enumeration by generating functions. For several types of random planar maps, this culminates in concentration results for the degree sequence. For parking functions, we determine the distribution of the defect, the most characteristic parameter. On the subject of randomized search heuristics, we present, improve, and unify different probabilistic methods and their applications. In this, special focus is given to potential functions and the analysis of the drift of stochastic processes. We apply these techniques to investigate the runtimes of evolutionary algorithms. In particular, we show for several classical problems in combinatorial optimization how drift analysis can be used in a uniform way to give bounds on the expected runtimes of evolutionary algorithms.Diese Dissertationsschrift beschäftigt sich mit der wahrscheinlichkeitstheoretischen Analyse von zufälligen kombinatorischen Strukturen und der Laufzeitanalyse randomisierter Suchheuristiken. Im Bereich der zufälligen Strukturen untersuchen wir zwei Klassen kombinatorischer Objekte. Dies sind zum einen die Klasse aller kombinatorischen Einbettungen planarer Graphen und zum anderen eine Klasse diskreter Funktionen mit bestimmten kombinatorischen Restriktionen (generalized parking functions). Für das Studium dieser Klassen entwickeln und verwenden wir zählkombinatorische Methoden die auf erzeugenden Funktionen basieren. Dies erlaubt uns, Konzentrationsresultate für die Gradsequenzen verschiedener Typen zufälliger kombinatorischer Einbettungen planarer Graphen zu erzielen. Darüber hinaus erhalten wir Konzentrationsresultate für den charakteristischen Parameter, den Defekt, zufälliger Instanzen der untersuchten diskreten Funktionen. Im Bereich der randomisierten Suchheuristiken präsentieren und erweitern wir verschiedene wahrscheinlichkeitstheoretische Methoden der Analyse. Ein besonderer Fokus liegt dabei auf der Analyse der Drift stochastischer Prozesse. Wir wenden diese Methoden in der Laufzeitanalyse evolutionärer Algorithmen an. Insbesondere zeigen wir, wie mit Hilfe von Driftanalyse die erwarteten Laufzeiten evolutionärer Algorithmen auf verschiedenen klassischen Problemen der kombinatorischen Optimierung auf einheitliche Weise abgeschätzt werden können

Theory grounded design of genetic programming and parallel evolutionary algorithms

Evolutionary algorithms (EAs) have been successfully applied to many problems and applications. Their success comes from being general purpose, which means that the same EA can be used to solve different problems. Despite that, many factors can affect the behaviour and the performance of an EA and it has been proven that there isn't a particular EA which can solve efficiently any problem. This opens to the issue of understanding how different design choices can affect the performance of an EA and how to efficiently design and tune one. This thesis has two main objectives. On the one hand we will advance the theoretical understanding of evolutionary algorithms, particularly focusing on Genetic Programming and Parallel Evolutionary algorithms. We will do that trying to understand how different design choices affect the performance of the algorithms and providing rigorously proven bounds of the running time for different designs. This novel knowledge, built upon previous work on the theoretical foundation of EAs, will then help for the second objective of the thesis, which is to provide theory grounded design for Parallel Evolutionary Algorithms and Genetic Programming. This will consist in being inspired by the analysis of the algorithms to produce provably good algorithm designs