SciELO - Scientific Electronic Library Online

 
vol.9 suppl.1Solución de inteligencia de negocio para métricas de gestión de proyectosTécnicas para el tratamiento de restricciones en el problema de conformación de equipos de proyectos de software índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Revista

Articulo

Indicadores

  • No hay articulos citadosCitado por SciELO

Links relacionados

  • No hay articulos similaresSimilares en SciELO

Compartir


Revista Cubana de Ciencias Informáticas

versión On-line ISSN 2227-1899

Resumen

FAJARDO CALDERIN, Jenny et al. Deceptive Dynamic Optimization Problems, experimentation with Metaheuristics. Rev cuba cienc informat [online]. 2015, vol.9, suppl.1, pp.98-109. ISSN 2227-1899.

In the optimization field there are a number of problems called NP, which are those that can be solved by a nondeterministic polynomial time algorithm for a resolution. Because the real world is not static, but dynamic, the need to bring these problems to test reality is created, hence arise dynamic optimization problems (PODs). One of the classic optimization problems that exist, are disappointing or deceptive problems, which are problems of test generation from binary XOR. They are called deceptive because the algorithms have a hard time getting improvements, because when solution is improved heuristic evaluation worsens the objective function. In recent years there has been an increasing interest in the modeling of dynamic optimization problems and their solution with metaheuristic algorithms. Therefore, the objective of this research is to analyze the behavior of the classical metaheuristics disappointing compared to dynamic problems, specifically with five disappointing functions and evaluating the performance of algorithms using non-parametric statistical test. Furthermore a comparison of the results of the best algorithm with two the state of art algorithms are usually done to solve dynamic optimization: Adaptive Hill Climbing Memetic Algorithm y Self Organized Random Immigrants Genetic Algorithm.

Palabras clave : dynamic optimization problems; deceptive; metaheuristics algorithm.

        · resumen en Español     · texto en Español     · Español ( pdf )

 

Creative Commons License Todo el contenido de esta revista, excepto dónde está identificado, está bajo una Licencia Creative Commons