Clasificación mediante enjambre de prototipos

Los Clasificadores por Vecino ms Prximo (K-NN) han recibido un impulso renovado con la aplicación de metaheursticas de búsqueda (evolutivas, etc.) que permiten optimizar su funcionamiento, mediante la optimización de atributos, reducción de prototipos, y evolución de medidas globales o locales de proximidad; ello permite desarrollar clasificadores robustos e insensibles al ruido, competitivos con otros paradigmas de clasificación. Se aporta a este campo un nuevo algoritmo denominado Clasificador mediante Enjambre de Prototipos (CEP o PSC). Se inspira en el algoritmo de Optimización mediante Enjambres de Partículas (PSO), pero introduce un nuevo enfoque que permite abordar problemas de clasificación de forma flexible y escalable. El algoritmo utiliza un enfoque de Michigan para codificar las posiciones de un conjunto de prototipos, que se desplazan por el espacio de los atributos del problema mediante interacciones de tipo atractivo y repulsivo caractersticas de PSO. Se comporta como un optimizador para una función de evaluación multimodal y dinámica, que mide la calidad de cada prototipo. Los resultados experimentales son competitivos con una variedad de algoritmos de referencia. Como conclusión, se proponen campos de aplicación alternativos, y se avanza la posibilidad de generalizar la propuesta como nueva perspectiva dentro de en la Inteligencia de Enjambre _____________________________________________Nearest Neighbor Classifiers are a subject of renewed interest with the application of search metaheuristics (such as evolutionary techniques), that may improve their performance by means of attribute selection, prototype reduction and global or local proximity measures; these methods provide the ability to develop robust and noise-insensitive classifiers that may be competitive with other classification paradigms. We contribute to this field a new algorithm, called Prototype Swarm Classifier. It is inspired in the Particle Swarm Optimization algorithm, but it develops a new approach that enables the algorithm to deal with classification problems in a flexible and scalable way. The algorithm uses a Michigan approach to encode positions of a set of prototypes, that move in attribute space using the attractive and repulsive forces that are characteristic of PSO. It behaves by optimization of a multimodal dynamic local fitness function, that measures the quality of each prototype. Experimental results are competitive with those of a set of reference algoritms. In the conclusions, we propose other fields of application, and we introduce the possibility of generalization of the proposal as a new perspective in the field of Swarm Intelligence

Building nearest prototype classifiers using a Michigan approach PSO

IEEE Swarm Intelligence Symposium. Honolulu, HI, 1-5 april 2007This paper presents an application of particle swarm optimization (PSO) to continuous classification problems, using a Michigan approach. In this work, PSO is used to process training data to find a reduced set of prototypes to be used to classify the patterns, maintaining or increasing the accuracy of the nearest neighbor classifiers. The Michigan approach PSO represents each prototype by a particle and uses modified movement rules with particle competition and cooperation that ensure particle diversity. The result is that the particles are able to recognize clusters, find decision boundaries and achieve stable situations that also retain adaptation potential. The proposed method is tested both with artificial problems and with three real benchmark problems with quite promising results

A comparison between the Pittsburgh and Michigan approaches for the binary PSO algorithm

IEEE Congress on Evolutionary Computation. Edimburgo, 5 september 2005This paper shows the performance of the binary PSO algorithm as a classification system. These systems are classified in two different perspectives: the Pittsburgh and the Michigan approaches. In order to implement the Michigan approach binary PSO algorithm, the standard PSO dynamic equations are modified, introducing a repulsive force to favor particle competition. A dynamic neighborhood, adapted to classification problems, is also defined. Both classifiers are tested using a reference set of problems, where both classifiers achieve better performance than many classification techniques. The Michigan PSO classifier shows clear advantages over the Pittsburgh one both in terms of success rate and speed. The Michigan PSO can also be generalized to the continuous version of the PSO

Electrification of domestic hot water to aid the integration of renewable energy in the California grid

Water heating in residential buildings, also known as domestic hot water (DHW), is the third largest use of energy after appliances and space conditioning. About 90% of the residential buildings in the state use natural gas fueled water heaters, 6% use electricity, and a small percent use liquefied petroleum gas (LPG) or solar water heaters. The current energy use associated with residential water heating is small relative to the total amount of energy consumption in the residential building sector, but it is still a contributor of greenhouse gas (GHG) emissions. Improving hot water systems can be beneficial for bill customer savings, energy use, and water savings. Heat pump water heaters (HPWH) can function as grid batteries by using the water tank capability of thermal storage. The use of aggregated electrical DHW systems to store extra electricity during peak generation times or during low utility time of use (TOU) rates has the potential to alleviate some of the curtailed renewable energy power generation sources in the California grid while reducing carbon emissions and customer cost. Water heating technology was simulated using the Building Energy Modeling software California Building Energy for Code Compliance (CBECC-Res) and the California Simulation Engine (CSE). Different climate zones were explored to compare the electricity needed for a water heater operation given the same input parameters of water draw profiles and building envelope. The results show the feasibility of using HPWH and ERWH technology to participate in demand response management programs. The demand response capability of HPWH and ERWH show that they could be useful tools to accommodate surplus energy from solar generation during the solar peak hours. Whether the demand response is implemented using traditional HPWH or ERWH units, the capability of the technology to act on control signals is a necessary condition for a successful program

AMPSO: A new Particle Swarm Method for Nearest Neighborhood Classification

Nearest prototype methods can be quite successful on many pattern classification problems. In these methods, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. In this paper, we first use the standard particle swarm optimizer (PSO) algorithm to find those prototypes. Second, we present a new algorithm, called adaptive Michigan PSO (AMPSO) in order to reduce the dimension of the search space and provide more flexibility than the former in this application. AMPSO is based on a different approach to particle swarms as each particle in the swarm represents a single prototype in the solution. The swarm does not converge to a single solution; instead, each particle is a local classifier, and the whole swarm is taken as the solution to the problem. It uses modified PSO equations with both particle competition and cooperation and a dynamic neighborhood. As an additional feature, in AMPSO, the number of prototypes represented in the swarm is able to adapt to the problem, increasing as needed the number of prototypes and classes of the prototypes that make the solution to the problem. We compared the results of the standard PSO and AMPSO in several benchmark problems from the University of California, Irvine, data sets and find that AMPSO always found a better solution than the standard PSO. We also found that it was able to improve the results of the Nearest Neighbor classifiers, and it is also competitive with some of the algorithms most commonly used for classification.This work was supported by the Spanish founded research Project MSTAR::UC3M, Ref: TIN2008-06491-C04-03 and CAM Project CCG06-UC3M/ESP-0774.Publicad

An adaptive Michigan approach PSO for nearest prototype classification

Proceedings of: Second International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2007, La Manga del Mar Menor, Spain, June 18-21, 2007.Nearest Prototype methods can be quite successful on many pattern classification problems. In these methods, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. In this paper we develop a new algorithm (called AMPSO), based on the Particle Swarm Optimization (PSO) algorithm, that can be used to find those prototypes. Each particle in a swarm represents a single prototype in the solution; the swarm evolves using modified PSO equations with both particle competition and cooperation. Experimentation includes an artificial problem and six common application problems from the UCI data sets. The results show that the AMPSO algorithm is able to find solutions with a reduced number of prototypes that classify data with comparable or better accuracy than the 1-NN classifier. The algorithm can also be compared or improves the results of many classical algorithms in each of those problems; and the results show that AMPSO also performs significantly better than any tested algorithm in one of the problems.This article has been financed by the Spanish founded research MEC project OPLINK::UC3M, Ref: TIN2005-08818-C04-02 and CAM project UC3M-TEC-05-029

Multiobjective algorithms to optimize broadcasting parameters in mobile Ad-hoc networks

Congress on Evolutionary Computation. Singapore, 25-28 September 2007A mobile adhoc network (MANETs) is a self-configuring network of mobile routers (and associated hosts). The routers tend to move randomly and organize themselves arbitrarily; thus, the network's wireless topology may change fast and unpredictably. Nowadays, these networks are having a great influence due to the fact that they can create networks without a specific infrastructure. In MANETs message broadcasting is critical to network existence and organization. The broadcasting strategy in MANETs can be optimized by defining a multiobjective problem whose inputs are the broadcasting algorithm's parameters and whose objectives are: reaching as many stations as possible, minimizing the network utilization, and reducing the makespan. The network can be simulated to obtain the expected response to a given set of parameters. In this paper, we face this multiobjective problem with two algorithms: Multiobjective Particle Swarm Optimization and ESN (Evolution Strategy with NSGAII). Both algorithms are able to find an accurate approximation to the Pareto optimal front that is the solution of the problem. ESN improves the results of MOPSO in terms of the set coverage and hypervolume metrics used for comparison