2017
Cintrano, Christian; Chicano, Francisco; Alba, Enrique
Robust Bi-objective Shortest Path Problem in Real Road Networks Incollection
In: International Conference on Smart Cities, Smart-CT 2017, pp. 128–136, Springer, Cham, 2017, ISBN: 978-3-319-59513-9.
Abstract | Links | BibTeX | Tags: Bi-objective shortest path, Multi-objective optimization, Robustness, Traffic road network
@incollection{Cintrano2017,
title = {Robust Bi-objective Shortest Path Problem in Real Road Networks},
author = {Christian Cintrano and Francisco Chicano and Enrique Alba},
doi = {10.1007/978-3-319-59513-9_13},
isbn = {978-3-319-59513-9},
year = {2017},
date = {2017-06-01},
booktitle = {International Conference on Smart Cities, Smart-CT 2017},
pages = {128--136},
publisher = {Springer, Cham},
edition = {Lecture No},
abstract = {Road journeys are one of our most frequent daily tasks. Despite we need them, these trips have some associated costs: time, money, pollution, etc. One of the usual ways of modeling the road network is as a graph. The shortest path problem consists in finding the path in a graph that minimizes a certain cost function. However, in real world applications, more than one objective must be optimized simultaneously (e.g. time and pollution) and the data used in the optimization is not precise: it contains errors. In this paper we propose a new mathematical model for the robust bi-objective shortest path problem. In addition, some empirical studies are included to illustrate the utility of our formulation.},
keywords = {Bi-objective shortest path, Multi-objective optimization, Robustness, Traffic road network},
pubstate = {published},
tppubtype = {incollection}
}
Road journeys are one of our most frequent daily tasks. Despite we need them, these trips have some associated costs: time, money, pollution, etc. One of the usual ways of modeling the road network is as a graph. The shortest path problem consists in finding the path in a graph that minimizes a certain cost function. However, in real world applications, more than one objective must be optimized simultaneously (e.g. time and pollution) and the data used in the optimization is not precise: it contains errors. In this paper we propose a new mathematical model for the robust bi-objective shortest path problem. In addition, some empirical studies are included to illustrate the utility of our formulation.
2016
Chicano, Francisco; Whitley, Darrell; Tinos, Renato
Efficient Hill Climber for Constrained Pseudo-Boolean Optimization Problems Conference
Proceedings of the Genetic and Evolutionary Computation Conference 2016, ACM ACM, New York, NY, USA, 2016, ISBN: 978-1-4503-4206-3.
Links | BibTeX | Tags: constraint handling, hamming ball hill climber, local search, Multi-objective optimization, vector MK landscapes
@conference{285,
title = {Efficient Hill Climber for Constrained Pseudo-Boolean Optimization Problems},
author = {Francisco Chicano and Darrell Whitley and Renato Tinos},
url = {http://hdl.handle.net/10630/11979},
doi = {10.1145/2908812.2908869},
isbn = {978-1-4503-4206-3},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference 2016},
publisher = {ACM},
address = {New York, NY, USA},
organization = {ACM},
keywords = {constraint handling, hamming ball hill climber, local search, Multi-objective optimization, vector MK landscapes},
pubstate = {published},
tppubtype = {conference}
}
Bravo, Yesnier; Ferrer, Javier; Luque, Gabriel; Alba, Enrique
Smart Mobility by Optimizing the Traffic Lights: A New Tool for Traffic Control Centers Incollection
In: Lecture Notes in Computer Science, 9704 , pp. 147–156, 2016, ISSN: 16113349.
Abstract | Links | BibTeX | Tags: Multi-objective optimization, smart mobility, Traffic lights planning
@incollection{Bravo2016,
title = {Smart Mobility by Optimizing the Traffic Lights: A New Tool for Traffic Control Centers},
author = {Yesnier Bravo and Javier Ferrer and Gabriel Luque and Enrique Alba},
url = {http://link.springer.com/10.1007/978-3-319-39595-1_15},
doi = {10.1007/978-3-319-39595-1_15},
issn = {16113349},
year = {2016},
date = {2016-01-01},
booktitle = {Lecture Notes in Computer Science},
volume = {9704},
pages = {147--156},
abstract = {Urban traffic planning is a fertile area of Smart Cities to improve efficiency, environmental care, and safety, since the traffic jams and congestion are one of the biggest sources of pollution and noise. Traffic lights play an important role in solving these problems since they control the flow of the vehicular network at the city. However, the increasing number of vehicles makes necessary to go from a local control at one single intersection to a holistic approach considering a large urban area, only possible using advanced computational resources and techniques. Here we propose HITUL, a system that supports the decisions of the traffic control managers in a large urban area. HITUL takes the real traffic conditions and compute optimal traffic lights plans using bio-inspired techniques and micro-simulations. We compare our system against plans provided by experts. Our solutions not only enable continuous traffic flows but reduce the pollution. A case study of Málaga city allows us to validate the approach and show its benefits for other cities as well.},
keywords = {Multi-objective optimization, smart mobility, Traffic lights planning},
pubstate = {published},
tppubtype = {incollection}
}
Urban traffic planning is a fertile area of Smart Cities to improve efficiency, environmental care, and safety, since the traffic jams and congestion are one of the biggest sources of pollution and noise. Traffic lights play an important role in solving these problems since they control the flow of the vehicular network at the city. However, the increasing number of vehicles makes necessary to go from a local control at one single intersection to a holistic approach considering a large urban area, only possible using advanced computational resources and techniques. Here we propose HITUL, a system that supports the decisions of the traffic control managers in a large urban area. HITUL takes the real traffic conditions and compute optimal traffic lights plans using bio-inspired techniques and micro-simulations. We compare our system against plans provided by experts. Our solutions not only enable continuous traffic flows but reduce the pollution. A case study of Málaga city allows us to validate the approach and show its benefits for other cities as well.
2013
Lopez-Herrejon, Roberto E; Chicano, Francisco; Ferrer, Javier; Egyed, Alexander; Alba, Enrique
Multi-objective Optimal Test Suite Computation for Software Product Line Pairwise Testing Inproceedings
In: IEEE International Conference on Software Maintenance, pp. 404–407, 2013, ISSN: 1063-6773.
Abstract | Links | BibTeX | Tags: Multi-objective optimization, Pairwise Testing, Software Product Lines
@inproceedings{Lopez-Herrejon2013,
title = {Multi-objective Optimal Test Suite Computation for Software Product Line Pairwise Testing},
author = {Roberto E Lopez-Herrejon and Francisco Chicano and Javier Ferrer and Alexander Egyed and Enrique Alba},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6676918 http://neo.lcc.uma.es/staff/javi/files/icsm2013.pdf http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6676918},
doi = {10.1109/ICSM.2013.58},
issn = {1063-6773},
year = {2013},
date = {2013-09-01},
booktitle = {IEEE International Conference on Software Maintenance},
pages = {404--407},
abstract = {Software Product Lines (SPLs) are families of related software products, which usually provide a large number of feature combinations, a fact that poses a unique set of challenges for software testing. Recently, many SPL testing approaches have been proposed, among them pair wise combinatorial techniques that aim at selecting products to test based on the pairs of feature combinations such products provide. These approaches regard SPL testing as an optimization problem where either coverage (maximize) or test suite size (minimize) are considered as the main optimization objective. Instead, we take a multi-objective view where the two objectives are equally important. In this exploratory paper we propose a zero-one mathematical linear program for solving the multi-objective problem and present an algorithm to compute the true Pareto front, hence an optimal solution, from the feature model of a SPL. The evaluation with 118 feature models revealed an interesting trade-off between reducing the number of constraints in the linear program and the runtime which opens up several venues for future research.},
keywords = {Multi-objective optimization, Pairwise Testing, Software Product Lines},
pubstate = {published},
tppubtype = {inproceedings}
}
Software Product Lines (SPLs) are families of related software products, which usually provide a large number of feature combinations, a fact that poses a unique set of challenges for software testing. Recently, many SPL testing approaches have been proposed, among them pair wise combinatorial techniques that aim at selecting products to test based on the pairs of feature combinations such products provide. These approaches regard SPL testing as an optimization problem where either coverage (maximize) or test suite size (minimize) are considered as the main optimization objective. Instead, we take a multi-objective view where the two objectives are equally important. In this exploratory paper we propose a zero-one mathematical linear program for solving the multi-objective problem and present an algorithm to compute the true Pareto front, hence an optimal solution, from the feature model of a SPL. The evaluation with 118 feature models revealed an interesting trade-off between reducing the number of constraints in the linear program and the runtime which opens up several venues for future research.