Buscar recursos académicos

There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (fi...

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...

The use of evolutionary and swarm intelligence algorithms, has become a very popular option to solve complex real-world optimization problems. However, in their original versions, these algorithms lack a mechanism to han...

The objective of the present study was to develop an oral timed-released press-coated tablet containing theophylline as a model drug. A D-optimal design of experiment was employed to systematically study the effect of te...

An attempt has been made in this paper to access the control layer of the General Packet Radio Service (GPRS) network base station (BS), using throughput optimization. We focus on the effect of packet transmissions on th...

The performance of two selection mechanisms used in the most popular variant of differential evolution, known as DE/rand/1/bin, are compared in the solution of constrained numerical optimization problems. Four performanc...

In this Ph. D thesis, the main significant contributions are: formulation of a new non-linear membership function using fuzzy approach to capture and describe vagueness in the technological coefficients of constraints in...

The aim of the present research was to study the optimisation of mevastatin production by Penicillium citrinum MTCC 1256. Optimization of fermentation medium was carried out by response surface methodology. Simultaneous ...

An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. ...

Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be...

A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of "no free lunch" (NFL) theorems are presented which establish that for any algori...

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark ...

The MATLAB toolbox YALMIP is introduced. It is described how YALMIP can be used to model and solve optimization problems typically occurring in systems and control theory. In this paper, free MATLAB toolbox YALMIP, devel...

Materias / palabras clave: Particle swarm optimization; Swarming (honey bee); Computer science; Multi-swarm optimization; Metaheuristic; Swarm behaviour; Snapshot (computer storage); Mathematical optimization; Algorithm; Artificial intelligence; M...

The Ant Colony Optimization (ACO) metaheuristic is a bio-inspired approach for hard combinatorial optimization problems for stationary and non-stationary environments. In the ACO metaheuristic, a colony of artificial ant...

Actualmente, el meprobamato no es un fármaco de amplio espectro comercial. Con el objetivo de obtener una formulación farmacéutica óptima para este fármaco u otros más competitivos, la metodología de diseños expe...

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. T...

The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark test...

Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algori...

Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been develope...

This paper presents a hybrid method to solve hard multiobjective problems. The proposed approach adopts an epsilon-constraint method which uses a Particle Swarm Optimizer to get points near of the true Pareto front. In t...

Previous article Next article An Algorithm for Least-Squares Estimation of Nonlinear ParametersDonald W. MarquardtDonald W. Marquardthttps://doi.org/10.1137/0111030PDFPDF PLUSBibTexSections ToolsAdd to favoritesExport Ci...