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Title: Optimization algorithms combining (meta)heuristics and mathematical programming and its application in engineering
Authors: Rodríguez, Nibaldo
Gupta, Abhishek
Zabala, Paula L.
Cabrera-Guerrero, Guillermo
Keywords: DRNTU::Engineering::Computer science and engineering
Mathematical Programming
(Meta)heuristic Methods
Issue Date: 2018
Source: Rodríguez, N., Gupta, A., Zabala, P. L., & Cabrera-Guerrero, G. (2018). Optimization algorithms combining (meta)heuristics and mathematical programming and its application in engineering. Mathematical Problems in Engineering, 2018, 3967457-. doi:10.1155/2018/3967457
Series/Report no.: Mathematical Problems in Engineering
Abstract: Complex optimization problems can be tackled by means of mathematical programming methods as well as by means of (meta)heuristic methods. On the one hand, mathematical programming methods give us a guarantee of optimality while (meta)heuristic methods do not. On the other hand, heuristic methods can handle large and complex optimization problems while mathematical programming methods tend to fail as the size of the optimization problem increases. Thus, it makes sense to combine these two strategies to obtain better solutions to the problem that is being addressed. During the last two decades or so, algorithms that either include mathematical programming solvers into (meta)heuristic frameworks or include (meta)heuristic concepts within mathematical programming methods have demonstrated to be very effective in solving large complex optimization problems. These hybrid algorithms are also called matheuristics. These kinds of algorithms have been successfully applied to a wide range of optimization problems arising in engineering. In this special issue, we aimed to highlight those new approaches that take advantage of the main features of both mathematical programming and heuristic algorithms to solve challenging optimization problems. We received 129 submissions from all around the world. From these, only 25 articles were accepted after a rigorous peer-reviewed process, that is, a 19% acceptance rate. In the following, we briefly introduce each paper and try to organise them based on their main focus.
ISSN: 1024-123X
DOI: 10.1155/2018/3967457
Schools: School of Computer Science and Engineering 
Research Centres: Data Science and Artificial Intelligence Research Centre 
Rights: © 2018 Nibaldo Rodríguez et al. (Published by Hindawi Publishing Corporation). This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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