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Jose A. Egea

Departamento de Matemática Aplicada y Estadística
Universidad Politécnica de Cartagena
Antiguo Edificio del Hospital de Marina
Escuela Técnica Superior de Ingeniería Industrial
30202-Cartagena (Spain)
josea.egea@upct.es
Assistant professor: Department of Applied Mathematics and Statistics - Technical University of Cartagena (UPCT), Spain.

Education:
- M.Eng. in Chemical Engineering. University of Murcia (Spain), 2003.
- European Doctor in Chemical Engineering. University of Vigo (Spain), 2008.

Research interests:
- Metaheuristics for the optimization of complex-processes
- Mathematical tools for the optimization of computationally expensive models

Journal articles

2012
2011
2010
J A Egea, R Marti, J R Banga (2010)  An evolutionary method for complex-process optimization   Computers & Operations Research 37: 49. 315-324  
Abstract: In this paper we present a new evolutionary method for complex-process optimization. It is partially based on the principles of the scatter search methodology, but it makes use of innovative strategies to be more\ effective in the context of complex-process optimization using a small number of tuning parameters. In particular, we introduce a new combination method based on path relinking, which considers a broader area around the population members than previous combination methods. We also use a population update method which improves the balance between intensification and diversification. New strategies to intensify the search and to escape from suboptimal solutions are also presented. The application of the proposed evolutionary algorithm to different sets of both state-of-the-art continuous global optimization and complex-process optimization problems reveals that it is robust and efficient for the type of problems intended to solve, outperforming the results obtained with other methods found in the literature. (C) 2009 Elsevier Ltd. All rights reserved.
Notes: Times Cited: 1
2009
M Schlüter, J A Egea, L T Antelo, A A Alonso, J R Banga (2009)  An extended ant colony optimization algorithm for integrated process and control system design   Industrial and Engineering Chemistry Research 48: 14. 6723-6738  
Abstract: The problem of integrated process and control system design is discussed in this paper. We formulate it as a mixed integer nonlinear programming problem subject to differential-algebraic constraints. This class of problems is frequently nonconvex and, therefore, local optimization techniques usually fail to locate the global solution. Here we propose a global optimization algorithm, based on an extension of the ant colony optimization metaheuristic, in order to solve this challenging class of problems in an efficient and robust way. The ideas of the methodology are explained and, on the basis of different full-plant case studies, the performance of the approach is evaluated. The first set of benchmark problems deal with the integrated design and control of two different wastewater treatment plants, consisting on both NLP and MINLP formulations. The last case study is the well-known Tennessee Eastman process. Numerical experiments with our new method indicate that we can achieve an improved performance in all cases. Additionally, our method outperforms several other recent competitive solvers for the challenging case studies considered. © 2009 American Chemical Society.
Notes: Export Date: 26 August 2009
2008
2007
2006

Book chapters

2012
2006

Conference papers

2012
2011
2010
2009
2008
2006
2005

PhD theses

2008
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