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Tardiness related objectives are of utmost importance in production systems when client satisfaction is a main goal of a company. These objectives measure the system response to the client requirements and rate manager´...

This paper presents the solution of a real-world constrained bi-objective mechanical design problem in presence of a dynamic constraint by using the Modified Bacterial Foraging Optimization Algorithm. This algorithm, or...

Fachgebiete / Stichwörter: Computers; Artificial Intelligence; General

Fachgebiete / Stichwörter: Ordenadores; General

Fachgebiete / Stichwörter: MURATA, YASUO; ACADEMIC PRESS; 330.0151 M972m

Fachgebiete / Stichwörter: HEYMAN, DANIEL P; MCGRAW-HILL; 003 H618s

Metaheuristics based on swarm intelligence simulate the behavior of a biological social system like as a flock of birds or a swarm of bees, and they have achieved important advances for solving optimization problems. In ...

This paper proposes a hybrid particle swarm approach called Simple Multi-Objective Particle Swarm Optimizer (SMOPSO) which incorporates Pareto dominance, an elitist policy, and two techniques to maintain diversity: a mut...

In this paper, we present an artifi cial immune system (AIS) based on the CLONALG algorithm for solving constrained (numerical) optimization problems. We develop a new mutation operator which produces large and small ste...

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 c...

Niche formation allows evolutionary algorithms to be used when the location and maintenance of multiple solutions appertaining to diverse areas of the phenotypic space is required. Consequently the application field can ...

In this article we analyze two kinds of metaheuristic algorithms applied to wind farm optimization. The basic idea is to utilize CHC (a sort of GA) and GPSO (a sort of PSO) algorithms to obtain an acceptable configurati...

This paper presents an enhanced Particle Swarm Optimizer approach, which is designed to solve numerical unconstrained optimization problems. The approach incorporates a dual population in an attempt to overcome the probl...

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 wit...

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 problema...

Large bandwidth on hand in WDM networks is the best choice for increasing tra c demand; although, routing and wavelength assignment (RWA) problems still remain a challenge. This work proposes a novel method to solve mult...

Particle Swarm Optimization (PSO) ha sido utilizado exitosamente en diferentes áreas como optimización mutidimensional y multiobjetivo, y entrenamiento de redes neuronales entre otras, pero existen actualmente pocos re...

En este artículo se presenta una versión del algoritmo de Particle Swarm Optimization que ha sido hibridizado con un operador dinámico de mutación y que implementa el modelo conocido como local best (l-best). El algo...

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 ...

The article describes the proposition and implementation of a demonstration, learning and decision support system for the resolution of combinatorial optimization problems under multiple objectives. The system brings tog...

We propose an architecture which supports the behavioral self-optimization of complex systems. In this architecture we bring together specification-based reasoning and the framework of ant colony optimization (ACO). By t...

In previous works the ability of CPS-MCPC (an evolutionary, co-operative, population search method with multiple crossovers per couple) to build well delineated Pareto fronts in diverse multiobjective optimization proble...

Given the large number of optimization problems that mankind faces, metaheuristics are very important strategies for the resolution of these problems. These strategies assess the suitability of the individuals, which rep...

In this paper, we present an algorithm inspired on the T-Cell model of the immune system, i.e., an artificial immune system (AIS). The proposed approach (called DTC) is intented to solve dynamic optimiza- tion problems. ...