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

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

Optimizing constrained problems through a T-Cell artificial immune system

In this paper, we present a new model of an artificial immune system (AIS), based on the process that suffers the T-Cell, it is called T-Cell Model. It 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-theart in the area), with respect to an AIS previously proposed and a self-organizing migrating genetic algorithm for constrained optimization (C-SOMGA).Facultad de Informátic

Hybridizing an immune artificial algorithm with simulated annealing for solving constrained optimization problems

In this paper, we present a modified version of an algorithm inspired on the T-Cell model, it is an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed model (TCSA) is increased with simulated annealing, for solving constrained (numerical) optimization problems. We validate our proposed approach with a set of test functions taken from the specialized literature. We indirectly compare our results with respect to GENOCOP III, a well known software based on genetic algorithmPresentado en el XII Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

A new approach to particle swarm optimization algorithm

Particularly interesting group consists of algorithms that implement co-evolution or co-operation in natural environments, giving much more powerful implementations. The main aim is to obtain the algorithm which operation is not influenced by the environment. An unusual look at optimization algorithms made it possible to develop a new algorithm and its metaphors define for two groups of algorithms. These studies concern the particle swarm optimization algorithm as a model of predator and prey. New properties of the algorithm resulting from the co-operation mechanism that determines the operation of algorithm and significantly reduces environmental influence have been shown. Definitions of functions of behavior scenarios give new feature of the algorithm. This feature allows self controlling the optimization process. This approach can be successfully used in computer games. 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Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering