Parallel genetic algorithms: a feasible distributed : Implementation

Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically parallel nature of genetic algorithms. By distributing the total population, these models ref1ects a bebaviour nearer to that of natural systems. A variety of parallel computer systems architectures can offer distinct support features for their implementation. Ibis paper shows sorne remarkable characteristics of parallel genetic algorithms, details of a feasible design and their implementation. A1so some results related to the island model are shown.Eje: Redes Neuronales. Algoritmos genéticosRed de Universidades con Carreras en Informática (RedUNCI

Sulfation and carbonation competition in the treatment of flue gas from a coal-based power plant by calcium hydroxide

In this work, a gas containing CO2 and SO2 at the usual concentrations on the coal combustion flue gas reacted with calcium hydroxide to evaluate and quantify the influence of SO2 on the CO2 capture and vice versa. This influence was quantified with a continuous gas analyzer and by thermogravimetry (TG). Results show that the CO2 retained increases in general as its concentration does and decreases as the SO2 concentration increases. A similar behavior was found for the SO2 retention at different CO2 concentrations being more relevant the influence of the presence of SO2 on the CO2 capture than the opposite one. Results suggest that for a high CO2 capture, SO2 should be eliminated previously. With respect to the reaction process it was found that the desulfurization product clearly identified was CaSO3·½H2O; in the reaction between Ca(OH)2 and CO2, CaCO3 is mainly obtained, the complex CaO·CO2 being another possible product synthesized in low amount. Gas analyzer shows that SO2 and CO2 react simultaneously and that a part of the CaCO3 reacts with the SO2 and releases CO2. Sulfation values calculated by TG and from the gas analyzer are very similar but the amount of CO2 captured is not possible to know clearly by TG due to the synthesis and decomposition of CaCO3 during the process. The study of the evolution of the sorbent porosity in the process reveals that the presence of both acid gases produces a lower blockage of the pores than when only one gas is present probably due to the generation of new pores in the reaction of CaCO3 and SO2.We are thankful to MICINN in Spain, National Plan for Scientific Research, Development and Innovation, for financial support under Project: MAT2010-18862 and to the University of Cantabria, Project Ref. 51.VP10.64005

Simulation of the operation of hydro plants in an electricity market using Agent-Based Models

The optimization and simulation of power systemscontinues to be an area of concern for electricity companies andresearchers worldwide namely considering the development ofelectricity markets and competition in the generation activityTherefore generation companies are devoting an increasingattention to market issues justifying the development of modelsto help them preparing bidding strategies to the day-aheadmarket. In this context, agent-based models have been reportedas a complement to optimization and equilibrium models whenthe problem is too complex to be analyzed by traditionalapproaches. This paper details an Agent-Based Model for anelectricity market considering a detailed modeling for hydrostations and presents some preliminary results taking theIberian Electricity Market as an example

Parallel genetic algorithms: a feasible distributed : Implementation

Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically parallel nature of genetic algorithms. By distributing the total population, these models ref1ects a bebaviour nearer to that of natural systems. A variety of parallel computer systems architectures can offer distinct support features for their implementation. Ibis paper shows sorne remarkable characteristics of parallel genetic algorithms, details of a feasible design and their implementation. A1so some results related to the island model are shown.Eje: Redes Neuronales. Algoritmos genéticosRed de Universidades con Carreras en Informática (RedUNCI

Alternative strategies for asynchronous migration-controlled schemes in parallel genetic algorithms

Migration of individuals allows a fruitful interaction between subpopulations in the island model, a well known distributed approach for evolutionary computing, where separate subpopulations evolve in parallel. This model is well suited for a distributed environment running a Single Program Multiple Data (SPMD) scheme. Here, the same Genetic Algorithm (GA) is replicated in many processors and attempting better convergence, through an expected improvement on genetic diversity, selected individuals are exchanged periodically. For exchanging, an individual is selected from a source subpopulation and then exported towards a target subpopulation. Usually, the imported string is accepted on arrival and then inserted into the target subpopulation. Our earlier experiments on controlled migration showed an improvement on results when contrasted against those obtained by conventional migration approaches. This paper describes extended implementations of alternative strategies to oversee migration in asynchronous schemes for an island model and enlarges a previous work on three processors with a set of softer testing functions [9]. All of them try to decrease the risk of premature convergence. A first strategy attempts to prevent unbalanced propagation of genotypes by applying an acceptance threshold parameter to each incoming string. A second one permits independent evolution of subpopulations and acts only when a possible stagnation is detected. In such condition an attempt to evade falling towards a local optimum is done by inserting an expected dissimilar individual to improve genetic diversity. A third alternative strategy combines both previous mentioned strategies. The results presented are those obtained on the functions that showed to be more difficult for the island model using a replication of a simple GA. A description of the corresponding system architecture supporting the PGA implementation is described and results for the parallel distributed approach among 3, 6 and 12 processors is discussed.Eje: Procesamiento distribuido y paralelo. Tratamiento de señalesRed de Universidades con Carreras en Informática (RedUNCI

Enhancing evolutionary algorithms through recombination and parallelism

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed.I Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Alternative strategies for asynchronous migration-controlled schemes in parallel genetic algorithm

Migration of individuals allows a fruitful interaction between subpopulations in the island model, a well known distributed approach for evolutionary computing, where separate subpopulations evolve in parallel. This model is well suited for a distributed environment running a Single Program Multiple Data (SPMD) scheme. Here, the same Genetic Algorithm (GA) is replicated in many processors and attempting better convergence, through an expected improvement on genetic diversity, selected individuals are exchanged periodically. For exchanging, an individual is selected from a source subpopulation and then exported towards a target subpopulation. Usually, the imported string is accepted on arrival and then inserted into the target subpopulation. Our earlier experiments on controlled migration showed an improvement on results when contrasted against those obtained by conventional migration approaches. This paper describes extended implementations of alternative strategies to oversee migration in asynchronous schemes for an island model and enlarges a previous work on three processors with a set of softer testing functions [9]. All of them try to decrease the risk of premature convergence. A first strategy attempts to prevent unbalanced p ropagation of genotypes by applying an acceptance threshold parameter to each incoming string. A second one permits independent evolution of subpopulations and acts only when a possible stagnation is detected. In such condition an attempt to evade falling towards a local optimum is done by inserting an expected d issimilar individual to improve genetic diversity. A third alternative strategy combines both previous mentioned strategies. The results presented are those obtained on the functions that showed to be more difficult for the island model using a replication of a simple GA. A description of the corresponding system architecture supporting the PGA implementation is described and results for the parallel distributed approach among 3, 6 and 12 processors is discussed.Facultad de Informátic

Enhancing evolutionary algorithms through recombination and parallelism

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed.Facultad de Informátic

Enhancing evolutionary algorithms through recombination and parallelism

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed.Facultad de Informátic

Parallel genetic algorithms: a feasible distributed : Implementation

Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically parallel nature of genetic algorithms. By distributing the total population, these models ref1ects a bebaviour nearer to that of natural systems. A variety of parallel computer systems architectures can offer distinct support features for their implementation. Ibis paper shows sorne remarkable characteristics of parallel genetic algorithms, details of a feasible design and their implementation. A1so some results related to the island model are shown.Eje: Redes Neuronales. Algoritmos genéticosRed de Universidades con Carreras en Informática (RedUNCI