LopezIbanez.bib
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@preamble{{\providecommand{\MaxMinAntSystem}{{$\cal MAX$--$\cal MIN$} {A}nt {S}ystem} } # {\providecommand{\Rpackage}[1]{#1} } # {\providecommand{\SoftwarePackage}[1]{#1} } # {\providecommand{\proglang}[1]{#1} }}
@techreport{IRIDIA-2011-003,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {The Automatic Design of Multi-Objective Ant Colony
Optimization Algorithms},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2011,
number = {TR/IRIDIA/2011-003},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-003.pdf},
note = {Published in IEEE Transactions on Evolutionary
Computation~\cite{LopStu2012tec}}
}
@techreport{LopDubStu2011irace,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and J{\'e}r{\'e}mie Dubois-Lacoste and Thomas St{\"u}tzle and Mauro Birattari },
title = {The {\Rpackage{irace}} package, Iterated Race for
Automatic Algorithm Configuration},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2011,
number = {TR/IRIDIA/2011-004},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-004.pdf}
}
@techreport{IRIDIA-2011-001,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles and Marco Laumanns },
title = {On Sequential Online Archiving of Objective Vectors},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2011,
number = {TR/IRIDIA/2011-001},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-001.pdf},
note = {This is a revised version of the one published in EMO 2011~\cite{LopKnoLau2011emo}}
}
@techreport{IRIDIA-2010-002,
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Paola Pellegrini and Michael Maur and Marco A. {Montes de Oca} and Mauro Birattari and Marco Dorigo },
title = {Parameter Adaptation in Ant Colony Optimization},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2010-002},
year = 2010,
month = jan,
note = {Published as a book chapter~\cite{StuLopPel2011autsea}}
}
@techreport{IRIDIA-2009-026,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Adaptive ``Anytime'' Two-Phase Local Search},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2010,
number = {TR/IRIDIA/2009-026},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2009-026r001.pdf},
note = {Published in the proceedings of LION 4~\cite{DubLopStu10:lion-bfsp}}
}
@techreport{IRIDIA-2010-019,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {A Hybrid {TP+PLS} Algorithm for Bi-objective
Flow-Shop Scheduling Problems},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2010,
number = {TR/IRIDIA/2010-019},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2010-019r001.pdf},
note = {Published in Computers \& Operations Research~\cite{DubLopStu2011cor}}
}
@techreport{IRIDIA-2010-022,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Improving the Anytime Behavior of Two-Phase Local
Search},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2010,
number = {TR/IRIDIA/2010-022},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2010-022r001.pdf},
note = {Published in Annals of Mathematics and Artificial Intelligence~\cite{DubLopStu2011amai}}
}
@techreport{IRIDIA-2009-015,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2009,
number = {TR/IRIDIA/2009-015},
month = may,
note = {Published as a book chapter~\cite{LopPaqStu09emaa}}
}
@techreport{IRIDIA-2009-019,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Analysis of Algorithmic Components for
Multiobjective Ant Colony Optimization: A Case Study
on the Biobjective {TSP}},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2009-019},
year = 2009,
month = jun,
note = {Published in the proceedings of Evolution Artificielle, 2009~\cite{LopStu09ea}}
}
@techreport{IRIDIA-2009-020,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Effective Hybrid Stochastic Local Search Algorithms
for Biobjective Permutation Flowshop Scheduling},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
number = {TR/IRIDIA/2009-020},
year = 2009,
month = jun,
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2009-020r001.pdf},
note = {Published in the proceedings of Hybrid Metaheuristics 2009~\cite{DubLopStu09:hm-bfsp}}
}
@techreport{LopBlu08:tsptw,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum },
title = {Beam-{ACO} Based on Stochastic Sampling: {A} Case
Study on the {TSP} with Time Windows},
institution = {Department LSI, Universitat Polit{\`e}cnica de Catalunya},
year = 2008,
number = {LSI-08-28},
note = {Extended version published in Computers \& Operations Research~\cite{LopBlu2010cor}}
}
@techreport{BluBleLop08:lcs,
author = { Christian Blum and Mar{\'\i}a J. Blesa and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Beam Search for the Longest Common Subsequence
Problem},
institution = {Department LSI, Universitat Polit{\`e}cnica de Catalunya},
year = 2008,
number = {LSI-08-29},
note = {Published in Computers \& Operations Research~\cite{BluBleLop09-BeamSearch-LCS}}
}
@techreport{CI-235-07,
author = { Nicola Beume and Carlos M. Fonseca and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Jan Vahrenhold },
title = {On the Complexity of Computing the Hypervolume
Indicator},
institution = {University of Dortmund},
year = 2007,
number = {CI-235/07},
month = dec,
note = {Published in IEEE Transactions on Evolutionary Computation~\cite{BeuFonLopPaqVah09:tec}}
}
@techreport{PaqFonLop06-CSI-klee,
author = { Lu{\'i}s Paquete and Carlos M. Fonseca and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {An optimal algorithm for a special case of {K}lee's
measure problem in three dimensions},
institution = {CSI, Universidade do Algarve},
year = 2006,
number = {CSI-RT-I-01/2006},
abstract = {The measure of the region dominated by (the maxima
of) a set of $n$ points in the positive $d$-orthant
has been proposed as an indicator of performance in
multiobjective optimization, known as the
hypervolume indicator, and the problem of computing
it efficiently is attracting increasing
attention. In this report, this problem is
formulated as a special case of Klee's measure
problem in $d$ dimensions, which immediately
establishes $O(n^{d/2}\log n)$ as a, possibly
conservative, upper bound on the required
computation time. Then, an $O(n log n)$ algorithm
for the 3-dimensional version of this special case
is constructed, based on an existing dimension-sweep
algorithm for the related maxima problem. Finally,
$O(n log n)$ is shown to remain a lower bound on the
time required by the hypervolume indicator for
$d>1$, which attests the optimality of the algorithm
proposed.},
note = {Superseded by paper in IEEE Transactions on Evolutionary Computation~\cite{BeuFonLopPaqVah09:tec}},
annote = {Proof of Theorem 3.1 is incorrect}
}
@techreport{PaqStuLop-IRIDIA-2005-029,
author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {On the design and analysis of {SLS} algorithms for
multiobjective combinatorial optimization problems},
institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
year = 2005,
number = {TR/IRIDIA/2005-029},
abstract = {Effective Stochastic Local Search (SLS) algorithms
can be seen as being composed of several algorithmic
components, each of which plays some specific role
with respect to overall performance. In this
article, we explore the application of experimental
design techniques to analyze the effect of different
choices for these algorithmic components on SLS
algorithms applied to Multiobjective Combinatorial
Optimization Problems that are solved in terms of
{P}areto optimality. This analysis is done using the
example application of SLS algorithms to the
biobjective Quadratic Assignment Problem and we show
also that the same choices for algorithmic
components can lead to different behavior in
dependence of various instance features, such as the
structure of input data and the correlation between
objectives.},
url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2005-029r001.pdf}
}
@techreport{LopPaqStu04:hybrid,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {Hybrid Population-based Algorithms for the
Bi-objective Quadratic Assignment Problem},
institution = {FG Intellektik, FB Informatik, TU Darmstadt},
year = 2004,
number = {AIDA--04--11},
month = dec,
note = {Published in Journal of Mathematical Modelling and Algorithms~\cite{LopPaqStu05:jmma}}
}
@phdthesis{LopezIbanezPhD,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Operational Optimisation of Water Distribution
Networks},
school = {School of Engineering and the Built Environment},
year = 2009,
address = {Edinburgh Napier University, UK},
url = {http://researchrepository.napier.ac.uk/3044/}
}
@phdthesis{LopezDiploma,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Multi-objective Ant Colony Optimization},
school = {Intellectics Group, Computer Science Department,
Technische Universit{\"a}t Darmstadt, Germany},
year = 2004,
type = {Diploma thesis}
}
@misc{BezLopStu12:ants-supp,
author = { Leonardo C. T. Bezerra and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {{Automatic Generation of MOACO Algorithms for the Biobjective Bidimensional Knapsack Problem: Supplementary material}},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2012-008/}},
year = 2012
}
@misc{DubLopStu2012:evocop-supp,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {{Supplementary Material: Pareto Local Search Variants for Anytime Bi-Objective Optimization}},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2012-004}},
year = 2012
}
@misc{DubLopStu2011:gecco-supp,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {{Supplementary material: Automatic Configuration of State-of-the-art Multi-objective Optimizers Using the TPLS+PLS Framework}},
howpublished = {\url{http://iridia.ulb.ac.be/supp/IridiaSupp2011-005}},
year = 2011
}
@misc{LopPaqStu2010:eaftools,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {{EAF} Graphical Tools},
year = 2010,
url = {http://iridia.ulb.ac.be/~manuel/eaftools},
anote = {\url{http://iridia.ulb.ac.be/~manuel/eaftools}},
annote = {These tools are described in the book chapter
``\emph{Exploratory analysis of stochastic local
search algorithms in biobjective
optimization}''~\cite{LopPaqStu09emaa}}
}
@inproceedings{LopPraPae08:WDSA,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Parallel Optimisation Of Pump Schedules With A
Thread-Safe Variant Of {EPANET} Toolkit},
booktitle = {Proceedings of the 10th Annual Water Distribution
Systems Analysis Conference (WDSA 2008)},
year = 2008,
editor = { Jakobus E. van Zyl and A. A. Ilemobade and H. E. Jacobs },
month = aug,
pdf = {doc/LopezPrasadPaechter-WDSA2008-official.pdf},
doi = {10.1061/41024(340)40},
publisher = {ASCE}
}
@inproceedings{LopPraPaech:ccwi2005,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Optimal Pump Scheduling: Representation and Multiple
Objectives},
booktitle = {Proceedings of the Eighth International Conference
on Computing and Control for the Water Industry
(CCWI 2005)},
pages = {117--122},
year = 2005,
editor = { Dragan A. Savic and Godfrey A. Walters and Roger King and Soon Thiam-Khu },
volume = 1,
address = {University of Exeter, UK},
pdf = {doc/LopPraPae05-ccwi.pdf},
month = sep
}
@inproceedings{PaqStuLop05mic,
author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Towards the Empirical Analysis of {SLS} Algorithms
for Multiobjective Combinatorial Optimization
Problems through Experimental Design},
editor = {Karl F. Doerner and Michel Gendreau and Peter
Greistorfer and Gutjahr, Walter J. and Richard F. Hartl and Marc Reimann },
booktitle = {6th Metaheuristics International Conference (MIC
2005)},
year = 2005,
pages = {739--746},
address = {Vienna, Austria},
abstract = { Stochastic Local Search (SLS) algorithms for
Multiobjective Combinatorial Optimization Problems
(MCOPs) typically involve the selection and
parameterization of many algorithm components whose
role with respect to their overall performance and
relation to certain instance features is often not
clear. In this abstract, we use a modular approach
for the design of SLS algorithms for MCOPs defined
in terms of {P}areto optimality and we present an
extensive analysis of SLS algorithms through
experimental design techniques, where each algorithm
component is considered a factor. The experimental
analysis is based on a sound experimental
methodology for analyzing the output of algorithms
for MCOPs. We show that different choices for
algorithm components can lead to different behavior
in dependence of various instance features.},
pdf = {PaqStuLop05mic.pdf}
}
@incollection{BluLop2011ieh,
author = { Christian Blum and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
booktitle = {The Industrial Electronics Handbook: Intelligent
Systems},
title = {Ant Colony Optimization},
publisher = {CRC Press},
year = {2011},
edition = {Second},
isbn = {9781439802830},
url = {http://www.crcpress.com/product/isbn/9781439802830},
annnote = {http://www.eng.auburn.edu/~wilambm/ieh/}
}
@incollection{PaqStuLop07metaheuristics,
author = { Lu{\'i}s Paquete and Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Using experimental design to analyze stochastic
local search algorithms for multiobjective problems},
booktitle = {Metaheuristics: Progress in Complex Systems
Optimization},
pages = {325--344},
year = 2007,
doi = {10.1007/978-0-387-71921-4_17},
volume = 39,
series = {Operations Research / Computer Science Interfaces},
publisher = {Springer, New York, NY},
annote = {Post-Conference Proceedings of the 6th
Metaheuristics International Conference (MIC 2005)},
editor = {Karl F. Doerner and Michel Gendreau and Peter
Greistorfer and Gutjahr, Walter J. and Richard F. Hartl and Marc Reimann },
abstract = {Stochastic Local Search (SLS) algorithms can be seen
as being composed of several algorithmic components,
each playing some specific role with respect to
overall performance. This article explores the
application of experimental design techniques to
analyze the effect of components of SLS algorithms
for Multiobjective Combinatorial Optimization
problems, in particular for the Biobjective
Quadratic Assignment Problem. The analysis shows
that there exists a strong dependence between the
choices for these components and various instance
features, such as the structure of the input data
and the correlation between the objectives.}
}
@incollection{StuLopPel2011autsea,
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Paola Pellegrini and Michael Maur and Marco A. {Montes de Oca} and Mauro Birattari and Marco Dorigo },
title = {Parameter Adaptation in Ant Colony Optimization},
crossref = {AUTSEA2011},
doi = {10.1007/978-3-642-21434-9_8},
pages = {191--215}
}
@incollection{StuLopDor2011eorms,
author = { Thomas St{\"u}tzle and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Marco Dorigo },
title = {A Concise Overview of Applications of Ant Colony
Optimization},
pages = {896--911},
volume = 2,
doi = {10.1002/9780470400531.eorms0001},
crossref = {EORMS2011}
}
@incollection{EppLopStuDeS2011:cec,
author = { Stefan Eppe and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle and Yves {De Smet} },
title = {An Experimental Study of Preference Model Integration into Multi-Objective Optimization Heuristics},
crossref = {CEC2011},
pages = {2751--2758}
}
@incollection{MauLopStu2010:cec,
author = { Michael Maur and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Pre-scheduled and adaptive parameter variation in
{\MaxMinAntSystem}},
pages = {3823--3830},
doi = {10.1109/CEC.2010.5586332},
crossref = {CEC2010}
}
@incollection{LopStu09ea,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {An Analysis of Algorithmic Components for
Multiobjective Ant Colony Optimization: {A} Case
Study on the Biobjective {TSP}},
crossref = {EA2009},
pages = {134--145},
doi = {10.1007/978-3-642-14156-0_12}
}
@incollection{LopStu2010:ants,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic Configuration of Multi-Objective {ACO}
Algorithms},
pages = {95--106},
crossref = {ANTS2010},
doi = {10.1007/978-3-642-15461-4_9},
abstract = {In the last few years a significant number of ant
colony optimization (ACO) algorithms have been
proposed for tackling multi-objective optimization
problems. In this paper, we propose a software
framework that allows to instantiate the most
prominent multi-objective ACO (MOACO)
algorithms. More importantly, the flexibility of
this MOACO framework allows the application of
automatic algorithm configuration techniques. The
experimental results presented in this paper show
that such an automatic configuration of MOACO
algorithms is highly desirable, given that our
automatically configured algorithms clearly
outperform the best performing MOACO algorithms that
have been proposed in the literature. As far as we
are aware, this paper is also the first to apply
automatic algorithm configuration techniques to
multi-objective stochastic local search algorithms.}
}
@incollection{LopStu2010:gecco,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {The impact of design choices of multi-objective ant
colony optimization algorithms on performance: An
experimental study on the biobjective {TSP}},
crossref = {GECCO2010},
doi = {10.1145/1830483.1830494},
pages = {71--78},
abstract = {Over the last few years, there have been a number of
proposals of ant colony optimization (ACO)
algorithms for tackling multiobjective combinatorial
optimization problems. These proposals adapt ACO
concepts in various ways, for example, some use
multiple pheromone matrices and multiple heuristic
matrices and others use multiple ant colonies.\\ In
this article, we carefully examine several of the
most prominent of these proposals. In particular, we
identify commonalities among the approaches by
recasting the original formulation of the algorithms
in different terms. For example, several proposals
described in terms of multiple colonies can be cast
equivalently using a single ant colony, where ants
use different weights for aggregating the pheromone
and/or the heuristic information. We study
algorithmic choices for the various proposals and we
identify previously undetected trade-offs in their
performance.}
}
@incollection{LopPraPae:gecco07,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Solving Optimal Pump Control Problem using
{\MaxMinAntSystem}},
volume = 1,
pages = 176,
crossref = {GECCO2007},
pdf = {doc/pap212s1-lopezibanez.pdf}
}
@incollection{LopPraPaech05:cec,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Multi-objective Optimisation of the Pump Scheduling
Problem using {SPEA2}},
crossref = {CEC2005},
pages = {435--442},
volume = 1,
doi = {10.1109/CEC.2005.1554716}
}
@incollection{LopPaqStu04:ants,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {On the Design of {ACO} for the Biobjective Quadratic
Assignment Problem},
pages = {214--225},
doi = {10.1007/978-3-540-28646-2_19},
crossref = {ANTS2004}
}
@incollection{LopPaqStu09emaa,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {Exploratory Analysis of Stochastic Local Search
Algorithms in Biobjective Optimization},
pages = {209--222},
doi = {10.1007/978-3-642-02538-9_9},
crossref = {BarChiPaqPre2010emaoa},
abstract = {This chapter introduces two Perl programs that
implement graphical tools for exploring the
performance of stochastic local search algorithms
for biobjective optimization problems. These tools
are based on the concept of the empirical attainment
function (EAF), which describes the probabilistic
distribution of the outcomes obtained by a
stochastic algorithm in the objective space. In
particular, we consider the visualization of
attainment surfaces and differences between the
first-order EAFs of the outcomes of two
algorithms. This visualization allows us to identify
certain algorithmic behaviors in a graphical way.
We explain the use of these visualization tools and
illustrate them with examples arising from
practice.}
}
@incollection{LopKnoLau2011emo,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Joshua D. Knowles and Marco Laumanns },
title = {On Sequential Online Archiving of Objective Vectors},
pages = {46--60},
doi = {10.1007/978-3-642-19893-9_4},
abstract = {In this paper, we examine the problem of maintaining
an approximation of the set of nondominated points
visited during a multiobjective optimization, a
problem commonly known as archiving. Most of the
currently available archiving algorithms are
reviewed, and what is known about their convergence
and approximation properties is summarized. The main
scenario considered is the restricted case where the
archive must be updated online as points are
generated one by one, and at most a fixed number of
points are to be stored in the archive at any one
time. In this scenario, the better-monotonicity of
an archiving algorithm is proposed as a weaker, but
more practical, property than negative efficiency
preservation. This paper shows that
hypervolume-based archivers and a recently proposed
multi-level grid archiver have this property. On the
other hand, the archiving methods used by SPEA2 and
NSGA-II do not, and they may better-deteriorate with
time. The better-monotonicity property has meaning
on any input sequence of points. We also classify
archivers according to limit properties,
i.e. convergence and approximation properties of the
archiver in the limit of infinite (input) samples
from a finite space with strictly positive
generation probabilities for all points. This paper
establishes a number of research questions, and
provides the initial framework and analysis for
answering them.},
crossref = {EMO2011}
}
@incollection{LopBlu09:evocop,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum and Dhananjay Thiruvady and Andreas T. Ernst and Bernd Meyer },
title = {Beam-{ACO} based on stochastic sampling for makespan
optimization concerning the {TSP} with time windows},
crossref = {EVOCOP2009},
pages = {97--108},
pdf = {LopBlu09-Beam-ACO-TSPTW-evocop.pdf},
doi = {10.1007/978-3-642-01009-5_9},
alias = {Lop++09}
}
@incollection{LopBlu09:lion,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum },
title = {Beam-{ACO} Based on Stochastic Sampling: {A} Case
Study on the {TSP} with Time Windows},
pages = {59--73},
doi = {10.1007/978-3-642-11169-3_5},
crossref = {LION2009}
}
@incollection{DubLopStu09:hm-bfsp,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Effective Hybrid Stochastic Local Search Algorithms
for Biobjective Permutation Flowshop Scheduling},
pages = {100--114},
pdf = {DubLopStu09hm-bfsp.pdf},
doi = {10.1007/978-3-642-04918-7_8},
crossref = {HM2009},
alias = {DuboisHM09}
}
@incollection{DubLopStu10:lion-bfsp,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Adaptive ``Anytime'' Two-Phase Local Search},
pages = {52--67},
doi = {10.1007/978-3-642-13800-3_5},
crossref = {LION2010}
}
@incollection{DubLopStu2011gecco,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Automatic configuration of state-of-the-art multi-objective
optimizers using the {TP+PLS} framework},
crossref = {GECCO2011},
pages = {2019--2026},
doi = {10.1145/2001576.2001847}
}
@incollection{DubLopStu2012evocop,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {{P}areto Local Search Algorithms for Anytime
Bi-objective Optimization},
crossref = {EVOCOP2012},
pages = {206--217},
doi = {10.1007/978-3-642-29124-1_18},
alias = {DubLopStu12:evocop}
}
@incollection{FonPaqLop06:hypervolume,
author = { Carlos M. Fonseca and Lu{\'i}s Paquete and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {An improved dimension\hspace{0pt}-\hspace{0pt}sweep
algorithm for the hypervolume indicator},
crossref = {CEC2006},
pages = {1157--1163},
doi = {10.1109/CEC.2006.1688440},
pdf = {FonPaqLop06-hypervolume.pdf},
abstract = {This paper presents a recursive, dimension-sweep
algorithm for computing the hypervolume indicator of
the quality of a set of $n$ non-dominated points in
$d>2$ dimensions. It improves upon the existing HSO
(Hypervolume by Slicing Objectives) algorithm by
pruning the recursion tree to avoid repeated
dominance checks and the recalculation of partial
hypervolumes. Additionally, it incorporates a recent
result for the three-dimensional special case. The
proposed algorithm achieves $O(n^{d-2} \log n)$ time
and linear space complexity in the worst-case, but
experimental results show that the pruning
techniques used may reduce the time complexity
exponent even further.}
}
@incollection{FonGueLopPaq2011emo,
author = { Carlos M. Fonseca and Andreia P. Guerreiro and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete },
title = {On the Computation of the Empirical Attainment Function},
crossref = {EMO2011},
doi = {10.1007/978-3-642-19893-9_8},
pages = {106--120}
}
@book{AUTSEA2011,
editor = {Y. Hamadi and E. Monfroy and F. Saubion},
title = {Autonomous Search},
booktitle = {Autonomous Search},
publisher = {Springer},
address = {Berlin, Germany},
year = 2012
}
@book{EVOCOP2012,
title = {Evolutionary Computation in Combinatorial Optimization -
12th European Conference, EvoCOP 2012, M{\'a}laga, Spain,
April 11-13, 2012. Proceedings},
booktitle = {Proceedings of EvoCOP 2012 -- 12th European Conference on Evolutionary Computation in Combinatorial Optimization},
editor = {Jin-Kao Hao and Martin Middendorf},
year = 2012,
volume = 7245,
series = {Lecture Notes in Computer Science},
publisher = {Springer, Heidelberg, Germany}
}
@book{CEC2011,
title = {Proceedings of the 2011 Congress on Evolutionary
Computation (CEC 2011)},
booktitle = {Proceedings of the 2011 Congress on Evolutionary
Computation (CEC 2011)},
publisher = {IEEE Press},
address = {Piscataway, NJ},
year = 2011
}
@book{EMO2011,
title = {Evolutionary Multi-Criterion Optimization. 6th
International Conference, EMO 2011},
booktitle = {Evolutionary Multi-criterion Optimization (EMO
2011)},
editor = { Takahashi, R. H. C. and others},
volume = 6576,
series = {Lecture Notes in Computer Science},
year = 2011,
publisher = {Springer, Heidelberg, Germany}
}
@book{EORMS2011,
title = {Wiley Encyclopedia of Operations Research and Management
Science},
booktitle = {Wiley Encyclopedia of Operations Research and
Management Science},
editor = {J. J. Cochran},
publisher = {John Wiley \& Sons},
year = 2011,
doi = {10.1002/9780470400531}
}
@book{GECCO2011,
title = {Genetic and Evolutionary Computation Conference,
GECCO 2011, Proceedings, Dublin, Ireland, July
12-16, 2011},
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2011},
editor = {N. Krasnogor and others},
year = 2011,
publisher = {ACM press},
address = {New York, NY}
}
@book{ANTS2010,
title = {Ant Colony Optimization and Swarm Intelligence, 7th
International Conference, ANTS 2010},
booktitle = {Swarm Intelligence, 7th International Conference, ANTS 2010},
year = 2010,
editor = { Marco Dorigo and others },
fulleditor = { Marco Dorigo and Mauro Birattari and Di Caro, G.A. and
Doursat, R. and Engelbrecht, A.P. and Floreano,
D. and Gambardella, L.M. and Gro\ss, R. and Sahin,
E. and Thomas St{\"u}tzle and Sayama, H.},
publisher = {Springer, Heidelberg, Germany},
series = {Lecture Notes in Computer Science},
volume = 6234
}
@book{BarChiPaqPre2010emaoa,
title = {Experimental Methods for the Analysis of
Optimization Algorithms},
booktitle = {Experimental Methods for the Analysis of
Optimization Algorithms},
publisher = {Springer},
address = {Berlin, Germany},
year = 2010,
editor = { Thomas Bartz-Beielstein and Marco Chiarandini and Lu{\'i}s Paquete and Mike Preuss }
}
@book{CEC2010,
editor = {Ishibuchi, H. and others},
title = {Proceedings of the 2010 Congress on Evolutionary
Computation (CEC 2010)},
booktitle = {Proceedings of the 2010 Congress on Evolutionary
Computation (CEC 2010)},
publisher = {IEEE Press},
address = {Piscataway, NJ},
year = 2010
}
@book{EA2009,
title = {Artificial Evolution: 9th International Conference,
Evolution Artificielle, EA, 2009, Strasbourg,
France, October 26-28, 2009. Revised Selected
Papers},
booktitle = {Artificial Evolution: 9th International Conference, Evolution Artificielle, EA, 2009},
year = 2010,
series = {Lecture Notes in Computer Science},
volume = 5975,
shorteditor = {Pierre Collet and others},
editor = {Pierre Collet and Nicolas Monmarch{\'e} and Pierrick
Legrand and Marc Schoenauer and Evelyne Lutton},
publisher = {Springer, Heidelberg, Germany}
}
@book{GECCO2010,
editor = {Martin Pelikan and J{\"u}rgen Branke},
title = {Genetic and Evolutionary Computation Conference,
GECCO 2010, Proceedings, Portland, Oregon, USA, July
7-11, 2010},
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2010},
year = 2010,
publisher = {ACM press},
address = {New York, NY}
}
@book{LION2010,
title = {4th International Conference, LION 4, Venice, Italy,
January 18-22, 2010. Selected Papers},
booktitle = {Learning and Intelligent Optimization, 4th
International Conference, LION 4},
year = 2010,
volume = 6073,
series = {Lecture Notes in Computer Science},
editor = { Christian Blum and Roberto Battiti },
publisher = {Springer, Heidelberg, Germany},
doi = {10.1007/978-3-642-13800-3}
}
@book{EVOCOP2009,
title = {Proceedings of EvoCOP 2009 -- 9th European Conference on Evolutionary Computation in Combinatorial Optimization},
booktitle = {Proceedings of EvoCOP 2009 -- 9th European Conference on Evolutionary Computation in Combinatorial Optimization},
editor = {C. Cotta and P. Cowling},
year = 2009,
volume = 5482,
series = {Lecture Notes in Computer Science},
publisher = {Springer, Heidelberg, Germany}
}
@book{HM2009,
title = {Hybrid Metaheuristics -- 6th International Workshop,
HM 2009},
booktitle = {Hybrid Metaheuristics},
year = 2009,
editor = { Mar{\'\i}a J. Blesa and Christian Blum and Luca {Di Gaspero} and Andrea Roli and M. Sampels and Andrea Schaerf},
series = {Lecture Notes in Computer Science},
volume = 5818,
publisher = {Springer, Heidelberg, Germany}
}
@book{LION2009,
title = {Third International Conference, LION 3, Trento,
Italy, January 14-18, 2009. Selected Papers},
booktitle = {Learning and Intelligent Optimization, Third International Conference, LION 3},
series = {Lecture Notes in Computer Science},
volume = 5851,
editor = { Thomas St{\"u}tzle },
year = 2009,
publisher = {Springer, Heidelberg, Germany}
}
@book{GECCO2007,
title = {GECCO'07: Proceedings of the 9th Annual Conference
on Genetic and Evolutionary Computation, London, UK},
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2007},
editor = {Dirk Thierens and others},
year = 2007,
publisher = {ACM press},
address = {New York, NY}
}
@book{CEC2006,
title = {Proceedings of the 2006 Congress on Evolutionary
Computation (CEC 2006)},
booktitle = {Proceedings of the 2006 Congress on Evolutionary
Computation (CEC 2006)},
year = 2006,
month = jul,
publisher = {IEEE Press},
address = {Piscataway, NJ}
}
@book{CEC2005,
title = {Proceedings of the 2005 Congress on Evolutionary
Computation (CEC 2005)},
booktitle = {Proceedings of the 2005 Congress on Evolutionary
Computation (CEC 2005)},
year = 2005,
month = sep,
publisher = {IEEE Press},
address = {Piscataway, NJ}
}
@book{ANTS2004,
title = {Ant Colony Optimization and Swarm Intelligence, 4th
International Workshop, ANTS 2004},
booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
International Workshop, ANTS 2004},
year = 2004,
fulleditor = { Marco Dorigo and L. M. Gambardella and F. Mondada and Thomas St{\"u}tzle and Mauro Birattari and Christian Blum },
editor = { Marco Dorigo and others },
volume = 3172,
series = {Lecture Notes in Computer Science},
publisher = {Springer, Heidelberg, Germany}
}
@article{LopStu2012tec,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {The Automatic Design of
Multi-Objective Ant Colony Optimization
Algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2012,
optvolume = {},
optnumber = {},
optpages = {},
optmonth = {},
doi = {10.1109/TEVC.2011.2182651},
note = {Accepted},
optannote = {}
}
@article{LopPraPae2011ec,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Representations and Evolutionary Operators for the
Scheduling of Pump Operations in Water Distribution
Networks},
journal = {Evolutionary Computation},
year = 2011,
doi = {10.1162/EVCO_a_00035},
volume = 19,
number = 3,
pages = {429--467},
abstract = {Reducing the energy consumption of water
distribution networks has never had more
significance. The greatest energy savings can be
obtained by carefully scheduling the operations of
pumps. Schedules can be defined either implicitly,
in terms of other elements of the network such as
tank levels, or explicitly by specifying the time
during which each pump is on/off. The traditional
representation of explicit schedules is a string of
binary values with each bit representing pump on/off
status during a particular time interval. In this
paper, we formally define and analyze two new
explicit representations based on time-controlled
triggers, where the maximum number of pump switches
is established beforehand and the schedule may
contain less switches than the maximum. In these
representations, a pump schedule is divided into a
series of integers with each integer representing
the number of hours for which a pump is
active/inactive. This reduces the number of
potential schedules compared to the binary
representation, and allows the algorithm to operate
on the feasible region of the search space. We
propose evolutionary operators for these two new
representations. The new representations and their
corresponding operations are compared with the two
most-used representations in pump scheduling,
namely, binary representation and level-controlled
triggers. A detailed statistical analysis of the
results indicates which parameters have the greatest
effect on the performance of evolutionary
algorithms. The empirical results show that an
evolutionary algorithm using the proposed
representations improves over the results obtained
by a recent state-of-the-art Hybrid Genetic
Algorithm for pump scheduling using level-controlled
triggers.}
}
@article{DubLopStu2011amai,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {Improving the Anytime Behavior of Two-Phase Local
Search},
journal = {Annals of Mathematics and Artificial Intelligence},
year = 2011,
volume = 61,
number = 2,
pages = {125--154},
doi = {10.1007/s10472-011-9235-0},
alias = {DubLopStu2010amai}
}
@article{DubLopStu2011cor,
author = { J{\'e}r{\'e}mie Dubois-Lacoste and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Thomas St{\"u}tzle },
title = {A Hybrid {TP$+$PLS} Algorithm for Bi-objective
Flow-Shop Scheduling Problems},
journal = {Computers \& Operations Research},
year = 2011,
volume = 38,
number = 8,
pages = {1219--1236},
doi = {10.1016/j.cor.2010.10.008}
}
@article{LopBlu2010cor,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Christian Blum },
title = {Beam-{ACO} for the travelling salesman problem with
time windows},
journal = {Computers \& Operations Research},
year = 2010,
doi = {10.1016/j.cor.2009.11.015},
volume = 37,
number = 9,
pages = {1570--1583},
keywords = {Ant colony optimization},
keywords = {Travelling salesman problem with time windows},
keywords = {Hybridization},
alias = {LopBlu09tsptw},
abstract = {The travelling salesman problem with time windows is
a difficult optimization problem that arises, for
example, in logistics. This paper deals with the
minimization of the travel-cost. For solving this
problem, this paper proposes a Beam-ACO algorithm,
which is a hybrid method combining ant colony
optimization with beam search. In general, Beam-ACO
algorithms heavily rely on accurate and
computationally inexpensive bounding information for
differentiating between partial solutions. This work
uses stochastic sampling as a useful alternative. An
extensive experimental evaluation on seven benchmark
sets from the literature shows that the proposed
Beam-ACO algorithm is currently a state-of-the-art
technique for the travelling salesman problem with
time windows when travel-cost optimization is
concerned.}
}
@article{BeuFonLopPaqVah09:tec,
author = { Nicola Beume and Carlos M. Fonseca and Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Jan Vahrenhold },
title = {On the complexity of computing the hypervolume
indicator},
journal = {IEEE Transactions on Evolutionary Computation},
year = 2009,
volume = 13,
number = 5,
pages = {1075--1082},
doi = {10.1109/TEVC.2009.2015575},
abstract = {The goal of multi-objective optimization is to find
a set of best compromise solutions for typically
conflicting objectives. Due to the complex nature of
most real-life problems, only an approximation to
such an optimal set can be obtained within
reasonable (computing) time. To compare such
approximations, and thereby the performance of
multi-objective optimizers providing them, unary
quality measures are usually applied. Among these,
the \emph{hypervolume indicator} (or
\emph{S-metric}) is of particular relevance due to
its favorable properties. Moreover, this indicator
has been successfully integrated into stochastic
optimizers, such as evolutionary algorithms, where
it serves as a guidance criterion for finding good
approximations to the Pareto front.\\ Recent results
show that computing the hypervolume indicator can be
seen as solving a specialized version of Klee's
Measure Problem. In general, Klee's Measure Problem
can be solved with $\mathcal{O}(n \log n +
n^{d/2}\log n)$ comparisons for an input instance of
size $n$ in $d$ dimensions; as of this writing, it
is unknown whether a lower bound higher than
$\Omega(n \log n)$ can be proven.\\ In this article,
we derive a lower bound of $\Omega(n\log n)$ for the
complexity of computing the hypervolume indicator in
any number of dimensions $d>1$ by reducing the
so-called \textsc{UniformGap} problem to it. For
the three dimensional case, we also present a
matching upper bound of $\mathcal{O}(n\log n)$
comparisons that is obtained by extending an
algorithm for finding the maxima of a point set.}
}
@article{BluBleLop09-BeamSearch-LCS,
author = { Christian Blum and Mar{\'\i}a J. Blesa and Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
title = {Beam search for the longest common subsequence
problem},
number = 12,
journal = {Computers \& Operations Research},
year = 2009,
pages = {3178--3186},
volume = 36,
doi = {10.1016/j.cor.2009.02.005},
pdf = {BluBleLop09-BeamSearch-LCS.pdf},
abstract = { The longest common subsequence problem is a
classical string problem that concerns finding the
common part of a set of strings. It has several
important applications, for example, pattern
recognition or computational biology. Most research
efforts up to now have focused on solving this
problem optimally. In comparison, only few works
exist dealing with heuristic approaches. In this
work we present a deterministic beam search
algorithm. The results show that our algorithm
outperforms the current state-of-the-art approaches
not only in solution quality but often also in
computation time.}
}
@article{LopPraPae08aco,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and T. Devi Prasad and Ben Paechter },
title = {Ant Colony Optimisation for the Optimal Control of
Pumps in Water Distribution Networks},
journal = {Journal of Water Resources Planning and Management, {ASCE}},
year = 2008,
volume = 134,
number = 4,
pages = {337--346},
publisher = {{ASCE}},
pdf = {LopezPrasadPaechter08-jwrpm.pdf},
aurl = {http://link.aip.org/link/?QWR/134/337/1},
doi = {10.1061/(ASCE)0733-9496(2008)134:4(337)}
}
@article{LopPaqStu05:jmma,
author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Lu{\'i}s Paquete and Thomas St{\"u}tzle },
title = {Hybrid Population-based Algorithms for the
Bi-objective Quadratic Assignment Problem},
journal = {Journal of Mathematical Modelling and Algorithms},
year = 2006,
volume = 5,
number = 1,
pages = {111--137},
pdf = {LopPaqStu04-techrepAIDA-04-11.pdf},
doi = {10.1007/s10852-005-9034-x},
alias = {LopPaqStu06:jmma},
abstract = {We present variants of an ant colony optimization
(MO-ACO) algorithm and of an evolutionary algorithm
(SPEA2) for tackling multi-objective combinatorial
optimization problems, hybridized with an iterative
improvement algorithm and the robust tabu search
algorithm. The performance of the resulting hybrid
stochastic local search (SLS) algorithms is
experimentally investigated for the bi-objective
quadratic assignment problem (bQAP) and compared
against repeated applications of the underlying
local search algorithms for several
scalarizations. The experiments consider structured
and unstructured bQAP instances with various degrees
of correlation between the flow matrices. We do a
systematic experimental analysis of the algorithms
using outperformance relations and the attainment
functions methodology to asses differences in the
performance of the algorithms. The experimental
results show the usefulness of the hybrid algorithms
if the available computation time is not too limited
and identify SPEA2 hybridized with very short tabu
search runs as the most promising variant.}
}