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

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

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

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

Un algoritmo basado en células T para resolver problemas de despacho de energía económico y dinámico

This paper presents the artificial immune system IA DED (Immune Algorithm Dynamic Economic Dispatch) to solve the Dynamic Economic Dispatch (DED) problem and the Dynamic Economic Emission Dispatch (DEED) problem. Our approach considers these as dynamic problems whose constraints change over time. IA DED is inspired on the activation process that T cells suffer in order to find partial solutions. The proposed approach is validated using several DED problems taken from specialized literature and one DEED problem. The latter is addressed by transforming a multi-objective problem into a single-objective problem by using a linear aggregating function that combines the (weighted) values of the objectives into a single scalar value. Our results are compared with respect to those obtained by other approaches taken from the specialized literature. We also provide some statistical analysis in order to determine the sensitivity of the performance of our proposed approach to its parameters. Part of this work was presented at the XXV Argentine Congress of Computer Science (CACIC), 2019.Facultad de Informátic

Un algoritmo basado en células T para resolver problemas de despacho de energía económico y dinámico

This paper presents the artificial immune system IA DED (Immune Algorithm Dynamic Economic Dispatch) to solve the Dynamic Economic Dispatch (DED) problem and the Dynamic Economic Emission Dispatch (DEED) problem. Our approach considers these as dynamic problems whose constraints change over time. IA DED is inspired on the activation process that T cells suffer in order to find partial solutions. The proposed approach is validated using several DED problems taken from specialized literature and one DEED problem. The latter is addressed by transforming a multi-objective problem into a single-objective problem by using a linear aggregating function that combines the (weighted) values of the objectives into a single scalar value. Our results are compared with respect to those obtained by other approaches taken from the specialized literature. We also provide some statistical analysis in order to determine the sensitivity of the performance of our proposed approach to its parameters. Part of this work was presented at the XXV Argentine Congress of Computer Science (CACIC), 2019.Facultad de Informátic

An Artificial Immune System for Solving Dynamic Economic Power Dispatch Problems

In this paper, we propose an artificial immune system called IA DED, which stands for Immune Algorithm Dynamic Economic Dispatch. It is designed for solving the Dynamic Economic Dispatch (DED) problem. Our approach considers the DED problem as a dynamic problem whose constraints change over time. IA DED considers the activation process that T cells suffer in order to find partial solutions. The proposed approach is validated using several DED problems taken from the specialized literature. Our results are compared with respect to those obtained by other approaches taken from the specialized literature. We also provide some statistical analysis in order to determine the sensitivity of the performance of our proposed approach to its parameters.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Un algoritmo basado en células T para resolver problemas de despacho de energía económico y dinámico

This paper presents the artificial immune system IA DED (Immune Algorithm Dynamic Economic Dispatch) to solve the Dynamic Economic Dispatch (DED) problem and the Dynamic Economic Emission Dispatch (DEED) problem. Our approach considers these as dynamic problems whose constraints change over time. IA DED is inspired on the activation process that T cells suffer in order to find partial solutions. The proposed approach is validated using several DED problems taken from specialized literature and one DEED problem. The latter is addressed by transforming a multi-objective problem into a single-objective problem by using a linear aggregating function that combines the (weighted) values of the objectives into a single scalar value. Our results are compared with respect to those obtained by other approaches taken from the specialized literature. We also provide some statistical analysis in order to determine the sensitivity of the performance of our proposed approach to its parameters. Part of this work was presented at the XXV Argentine Congress of Computer Science (CACIC), 2019.Facultad de Informátic

Artificial immune system for solving global optimization problems

En este trabajo, se presenta un nuevo modelo de Sistema Inmune Artificial (SIA) basado en los procesos que sufren las células T para resolver problemas de optimización global. El modelo, denominado MCT, trabaja sobre cuatro poblaciones con diferentes representaciones para las células y cada población atraviesa por distintos procesos. Se validó el modelo con 23 funciones tomadas de la literatura especializada. El modelo es comparado con diferentes enfoques bio-inspirados.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 global optimization problems. The model operates on four populations: Virgins, Effectors (CD4 and CD8) and Memory. Each of them has a different role, representation and procedures. We validate our proposed approach with a set of test functions taken from the specialized literature and we also compare our results with the results obtained by different bio-inspired approachesWorkshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI