A comparison between centralized and decentralized genetic algorithms for the identical parallel machines scheduling

Identical parallel machines problems (Pm) involve task assignments to the system's resources (a machine bank in parallel). The basic model consists of m machines and n tasks. The tasks are assigned according to the availability of the resources, following some allocation rule. In this work, the minimization of some objectives related to the due dates such as the maximum tardiness (Tmax) and the average tardiness (Tavg) were dealt with centralized and decentralized evolutive algorithms (EAs). In order to test our algorithms we used standard benchmarks. The main goal of this research was determinate the quality of the results obtained with a centralized GA and three decentralized GAs used to solve parallel machines scheduling problems. The results were compared using the ANOVA statistic method.Red de Universidades con Carreras en Informática (RedUNCI

Multi-objective optimization with a Gaussian PSO algorithm

Particle Swarm Optimization es una heurística popular usada para resolver adecuada y efectivamente problemas mono-objetivo. En este artículo, presentamos una primera adaptación de esta heurística para tratar problemas multi-objetivo sin restricciones. La propuesta (llamada G-MOPSO) incorpora una actualización Gaussiana, dominancia Pareto, una política elitista, un archivo externo y un shake-mecanismo para mantener la diversidad. Para validar nuestro algoritmo, usamos cuatro funciones de prueba bien conocidas, con diferentes características. Los resultados preliminares son comparados con los valores obtenidos por un algoritmo evolutivo multi-objetivo representativo del estado del arte en el área: NSGA-II. También comparamos los resultados con los obtenidos por OMOPSO, un algoritmo multi-objetivo basado en la heurística PSO. La performance de nuestra propuesta es comparable con la de NSGA-II y supera a la de OMOPSOParticle Swarm Optimization is a popular heuristic used to solve suitably and effectively mono-objective problems. In this paper, we present an adaptation of this heuristic to treat unconstrained multi-objective problems. The proposed approach (called G-MOPSO) incorporates a Gaussian update of individuals, Pareto dominance, an elitist policy, and a shake-mechanism to maintain diversity. In order to validate our algorithm, we use four well-known test functions with different characteristics. Preliminary results are compared with respect to those obtained by a multi-objective evolutionary algorithm representative of the state-of-the-art: NSGA-II. We also compare the results with those obtained by OMOPSO, a multi-objective PSO based algorithm. The performance of our approach is comparable with the NSGA-II and outperforms the OMOPSO.Workshop de Agentes y Sistemas Inteligentes (WASI)Red 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

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

Evolutionary optimization in dynamic fitness landscape environments

Non-stationary, or dynamic, problems change over time. There exist a variety of forms of dynamism. The concept of dynamic environments in the context of this paper means that the fitness landscape changes during the run of an evolutionary algorithm. Genetic diversity is crucial to provide the necessary adaptability of the algorithm to unexpected changes. Two key concepts to maintain genetic diversity in the population are incorporated to the algorithm and proposed here: macromutation operators and random immigrants. The algorithm was tested on a set of dynamic testing functions provided by a dynamic fitness problem generator. The main goal was to determine the algorithm ability to face changes and dimensional or multimodal scalability in the functions. The effectiveness and limitations of the proposed algorithm in diverse scenarios of a dynamic environment is discussed from results empirically obtained.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

Evolutionary optimization in dynamic fitness landscape environments

Non-stationary, or dynamic, problems change over time. There exist a variety of forms of dynamism. The concept of dynamic environments in the context of this paper means that the fitness landscape changes during the run of an evolutionary algorithm. Genetic diversity is crucial to provide the necessary adaptability of the algorithm to unexpected changes. Two key concepts to maintain genetic diversity in the population are incorporated to the algorithm and proposed here: macromutation operators and random immigrants. The algorithm was tested on a set of dynamic testing functions provided by a dynamic fitness problem generator. The main goal was to determine the algorithm ability to face changes and dimensional or multimodal scalability in the functions. The effectiveness and limitations of the proposed algorithm in diverse scenarios of a dynamic environment is discussed from results empirically obtained.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

Immune Algorithm for Solving the Dynamic Economic Dispatch Problem

In this paper, an algorithm inspired on the immune system is presented, IA DED stands for Immune Algorithm Dynamic Economic Dispatch, it is used to solve the Dynamic Economic Dispatch problem. IA DED uses as differentiation process a redistribution power operator and the output power are integer values. The proposed approach is val- idated using three problems taken from the specialized literature. Our results are compared with respect to those obtained by several other approaches.XVII Workshop Agentes y Sistemas Inteligentes (WASI).Red 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.Facultad de Informátic

Solving constrained optimization using a T-Cell artificial immune system

In this paper, we present a novel model of an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed model is used for solving constrained (numerical) optimization problems. The model operates on three populations: Virgins, Effectors and Memory. Each of them has a different role. Also, the model dynamically adapts the tolerance factor in order to improve the exploration capabilities of the algorithm. We also develop a new mutation operator which incorporates knowledge of the problem. We validate our proposed approach with a set of test functions taken from the specialized literature and we compare our results with respect to Stochastic Ranking (which is an approach representative of the state-of-the-art in the area) and with respect to an AIS previously proposed.En este trabajo, se presenta un nuevo modelo de Sistema Inmune Artificial (SIA), basado en el proceso que sufren las células T. El modelo propuesto se usa para resolver problemas de optimización (numéricos) restringidos. El modelo trabaja sobre tres poblaciones: Vírgenes, Efectoras y de Memoria. Cada una de ellas tiene un rol diferente. Además, el modelo adapta dinamicamente el factor de tolerancia para mejorar las capacidades de exploración del algoritmo. Se desarrolló un nuevo operador de mutación el cual incorpora conocimiento del problema. El modelo fue validado con un conjunto de funciones de prueba tomado de la literatura especializada y se compararon los resultados con respecto a Stochastic Ranking (el cual es un enfoque representativo del estado del arte en el área) y con respecto a un SIA propuesto previamente.VIII Workshop de Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Immune Algorithm for Solving the Dynamic Economic Dispatch Problem

In this paper, an algorithm inspired on the immune system is presented, IA DED stands for Immune Algorithm Dynamic Economic Dispatch, it is used to solve the Dynamic Economic Dispatch problem. IA DED uses as differentiation process a redistribution power operator and the output power are integer values. The proposed approach is val- idated using three problems taken from the specialized literature. Our results are compared with respect to those obtained by several other approaches.XVII Workshop Agentes y Sistemas Inteligentes (WASI).Red de Universidades con Carreras en Informática (RedUNCI