DSpace Collection:
http://hdl.handle.net/2117/3321
Fri, 22 May 2015 16:08:37 GMT2015-05-22T16:08:37Zwebmaster.bupc@upc.eduUniversitat Politècnica de Catalunya. Servei de Biblioteques i DocumentaciónoA biased random-key genetic algorithm for the capacitated minimum spanning tree problem
http://hdl.handle.net/2117/27676
Title: A biased random-key genetic algorithm for the capacitated minimum spanning tree problem
Authors: Ruiz Ruiz, Hector Efrain; Albareda Sambola, Maria; Fernández Aréizaga, Elena; Resende, Mauricio G. C.
Abstract: This paper focuses on the capacitated minimum spanning tree(CMST)problem.Given a central
processor and a set of remote terminals with specified demands for traffic that must flow between the central processor and terminals,the goal is to design a minimum cost network to carry this demand.
Potential links exist between any pair of terminals and between the central processor and the terminals.
Each potential link can be included in the design at a given cost.The CMST problem is to design a
minimum-cost network connecting the terminals with the central processor so that the flow on any arc of the network is at most Q. A biased random-keygenetic algorithm(BRKGA)is a metaheuristic for combinatorial optimization which evolves a population of random vectors that encode solutions to the combinatorial optimization problem.This paper explores several solution encodings as well as different
strategies for some steps of the algorithm and finally proposes a BRKGA heuristic for the CMST problem.
Computational experiments are presented showing the effectivenes sof the approach:Seven newbest-
known solutions are presented for the set of benchmark instances used in the experiments.Wed, 29 Apr 2015 16:50:22 GMThttp://hdl.handle.net/2117/276762015-04-29T16:50:22ZRuiz Ruiz, Hector Efrain; Albareda Sambola, Maria; Fernández Aréizaga, Elena; Resende, Mauricio G. C.noOptimization, Combinatorial optimization, Networks, Graphs, Trees, Spanning trees, Capacitated minimumspanningtree, Heuristics, Biased random-keygeneticalgorithmThis paper focuses on the capacitated minimum spanning tree(CMST)problem.Given a central
processor and a set of remote terminals with specified demands for traffic that must flow between the central processor and terminals,the goal is to design a minimum cost network to carry this demand.
Potential links exist between any pair of terminals and between the central processor and the terminals.
Each potential link can be included in the design at a given cost.The CMST problem is to design a
minimum-cost network connecting the terminals with the central processor so that the flow on any arc of the network is at most Q. A biased random-keygenetic algorithm(BRKGA)is a metaheuristic for combinatorial optimization which evolves a population of random vectors that encode solutions to the combinatorial optimization problem.This paper explores several solution encodings as well as different
strategies for some steps of the algorithm and finally proposes a BRKGA heuristic for the CMST problem.
Computational experiments are presented showing the effectivenes sof the approach:Seven newbest-
known solutions are presented for the set of benchmark instances used in the experiments.Stochastic model for electrical loads in Mediterranean residential building: validation and applications
http://hdl.handle.net/2117/27403
Title: Stochastic model for electrical loads in Mediterranean residential building: validation and applications
Authors: Ortiz, Joana Aina; Guarino, Francesco; Salom Tormo, Jaume; Corchero García, Cristina; Cellura, Maurizio
Abstract: A major issue in modelling the electrical load of residential building is reproducing the variability between dwellings due to the stochastic use of different electrical equipment. In that sense and with the objective to reproduce this variability, a stochastic model to obtain load profiles of household electricity is developed. The model is based on a probabilistic approach and is developed using data from the Mediterranean region of Spain. A detailed validation of the model has been done, analysing and comparing the results with Spanish and European data. The results of the validation show that the model is able to reproduce the most important features of the residential electrical consumption, especially the particularities of the Mediterranean countries. The final part of the paper is focused on the potential applications of the models, and some examples are proposed. The model is useful to simulate a cluster of buildings or individual households. The model allows obtaining synthetic profiles representing the most important characteristics of the mean dwelling, by means of a stochastic approach. The inputs of the proposed model are adapted to energy labelling information of the electric devices. An example case is presented considering a dwelling with high performance equipment.Thu, 16 Apr 2015 18:55:29 GMThttp://hdl.handle.net/2117/274032015-04-16T18:55:29ZOrtiz, Joana Aina; Guarino, Francesco; Salom Tormo, Jaume; Corchero García, Cristina; Cellura, MaurizionoStochastic model, Electric load, Residential building, Mediterranean region, Cluster of buildings, Energy labellingA major issue in modelling the electrical load of residential building is reproducing the variability between dwellings due to the stochastic use of different electrical equipment. In that sense and with the objective to reproduce this variability, a stochastic model to obtain load profiles of household electricity is developed. The model is based on a probabilistic approach and is developed using data from the Mediterranean region of Spain. A detailed validation of the model has been done, analysing and comparing the results with Spanish and European data. The results of the validation show that the model is able to reproduce the most important features of the residential electrical consumption, especially the particularities of the Mediterranean countries. The final part of the paper is focused on the potential applications of the models, and some examples are proposed. The model is useful to simulate a cluster of buildings or individual households. The model allows obtaining synthetic profiles representing the most important characteristics of the mean dwelling, by means of a stochastic approach. The inputs of the proposed model are adapted to energy labelling information of the electric devices. An example case is presented considering a dwelling with high performance equipment.Renewable energies in medium-term power planning
http://hdl.handle.net/2117/26280
Title: Renewable energies in medium-term power planning
Authors: Marí Tomàs, Laura; Nabona Francisco, Narcís
Abstract: The medium-term generation planning over a yearly horizon for generation portfolios including hydro generation and non-dispatchable renewables such as wind power and solar photovoltaic generation reveals how the penetration of these technologies reduces the share of other technologies and how it changes the profits and the profit spread in liberalized electricity markets. The matching of the load duration curves in different periods and the forced outages of generation units are here addressed through probabilistic methods and a heuristic is employed to guess which of the load-matching constraints may be active at the solution. There are well-established models for hydro generation in medium-term planning, but specific models are necessary to account for wind power and photovoltaic generation. A proposal for these is made in this work, which is suitable for use in the load duration curve matching through probabilistic methods. Moreover the stochasticity of the renewable sources requires the use of stochastic programming employing scenario trees, here developed using quasi-Monte Carlo techniques. Medium-term pumping schemes are also considered. Several realistic cases will be solved for two behavioral principles of a pure pool market: endogenous cartel and equilibrium.Tue, 10 Feb 2015 11:08:41 GMThttp://hdl.handle.net/2117/262802015-02-10T11:08:41ZMarí Tomàs, Laura; Nabona Francisco, NarcísnoLoad matching, medium-term power planning, renewable energy sources, stochastic programmingThe medium-term generation planning over a yearly horizon for generation portfolios including hydro generation and non-dispatchable renewables such as wind power and solar photovoltaic generation reveals how the penetration of these technologies reduces the share of other technologies and how it changes the profits and the profit spread in liberalized electricity markets. The matching of the load duration curves in different periods and the forced outages of generation units are here addressed through probabilistic methods and a heuristic is employed to guess which of the load-matching constraints may be active at the solution. There are well-established models for hydro generation in medium-term planning, but specific models are necessary to account for wind power and photovoltaic generation. A proposal for these is made in this work, which is suitable for use in the load duration curve matching through probabilistic methods. Moreover the stochasticity of the renewable sources requires the use of stochastic programming employing scenario trees, here developed using quasi-Monte Carlo techniques. Medium-term pumping schemes are also considered. Several realistic cases will be solved for two behavioral principles of a pure pool market: endogenous cartel and equilibrium.Comparative study of RPSALG algorithm for convex semi-infinite programming
http://hdl.handle.net/2117/24133
Title: Comparative study of RPSALG algorithm for convex semi-infinite programming
Authors: Auslander, Alfred; Ferrer Biosca, Alberto; Goberna, Miguel Ángel; López Cerdá, Marco Antonio
Abstract: The Remez penalty and smoothing algorithm (RPSALG) is a unified framework for penalty and smoothing methods for solving min-max convex semi-infinite programing problems, whose convergence was analyzed in a previous paper of three of the authors. In this paper we consider a partial implementation of RPSALG for solving ordinary convex semi-infinite programming problems. Each iteration of RPSALG involves two types of
auxiliary optimization problems: the Örst one consists of obtaining an approximate solution of some discretized convex problem, while the second one requires to solve a non-convex optimization problem involving the parametric constraints as objective function with the parameter as variable. In this paper we tackle the latter problem with a variant of the cutting angle method called ECAM, a global optimization procedure for solving Lipschitz programming problems. We implement di§erent variants of RPSALG which are compared with the unique publicly available SIP solver, NSIPS, on a battery of test problems.Mon, 22 Sep 2014 11:36:14 GMThttp://hdl.handle.net/2117/241332014-09-22T11:36:14ZAuslander, Alfred; Ferrer Biosca, Alberto; Goberna, Miguel Ángel; López Cerdá, Marco AntonionoConvex semi-infinite programming, Remez-type methods, penalty methods, smoothing methods, cutting angle method.The Remez penalty and smoothing algorithm (RPSALG) is a unified framework for penalty and smoothing methods for solving min-max convex semi-infinite programing problems, whose convergence was analyzed in a previous paper of three of the authors. In this paper we consider a partial implementation of RPSALG for solving ordinary convex semi-infinite programming problems. Each iteration of RPSALG involves two types of
auxiliary optimization problems: the Örst one consists of obtaining an approximate solution of some discretized convex problem, while the second one requires to solve a non-convex optimization problem involving the parametric constraints as objective function with the parameter as variable. In this paper we tackle the latter problem with a variant of the cutting angle method called ECAM, a global optimization procedure for solving Lipschitz programming problems. We implement di§erent variants of RPSALG which are compared with the unique publicly available SIP solver, NSIPS, on a battery of test problems.Solving DC programs using the cutting angle method
http://hdl.handle.net/2117/21984
Title: Solving DC programs using the cutting angle method
Authors: Ferrer Biosca, Alberto; Bagirov, Adil; Beliakov, Gleb
Abstract: In this paper, we propose a new algorithm for global minimization of functions represented as a difference of two convex functions. The proposed method is a derivative free method and it is designed by adapting the extended cutting angle method. We present preliminary results of numerical experiments using test problems with difference of convex objective functions and box-constraints. We also compare the proposed algorithm with a classical one that uses prismatical subdivisions.Tue, 11 Mar 2014 08:26:15 GMThttp://hdl.handle.net/2117/219842014-03-11T08:26:15ZFerrer Biosca, Alberto; Bagirov, Adil; Beliakov, GlebnoDC programming, Lipschitz programming, Cutting Angle methodIn this paper, we propose a new algorithm for global minimization of functions represented as a difference of two convex functions. The proposed method is a derivative free method and it is designed by adapting the extended cutting angle method. We present preliminary results of numerical experiments using test problems with difference of convex objective functions and box-constraints. We also compare the proposed algorithm with a classical one that uses prismatical subdivisions.Medium-term load matching in power planning
http://hdl.handle.net/2117/20720
Title: Medium-term load matching in power planning
Authors: Nabona Francisco, Narcís
Abstract: The medium-term planning for a company participating in a pure pool market can be considered under different behavioral
principles: cartel, endogenous cartel with respect to hydro generation, and equilibrium. One of the important aspects of the model is the medium-term load matching, where load is represented by the load duration curves of each period into which the
medium-term horizon is subdivided.
The probabilistically exact Bloom and Gallant formulation of load matching could be employed, but its exponential number of
load-matching inequality constraints limits its use to small cases.
A heuristic has been proposed where only a small subset of the load-matching constraints, hopefully containing the active ones at the optimizer, is employed. This heuristic does not perform totally well with the more complex models, as those of endogenous cartel and equilibrium behavior, and a more comprehensive heuristic procedure is presented here. A well-known alternative method of medium-term load matching is the multi-block approximation to the load duration curve.
The three load-matching techniques mentioned above are here described and are compared, and computational results for realistic
systems are analyzed for the different behavioral models, showing the advantages of each procedure.Mon, 25 Nov 2013 12:39:49 GMThttp://hdl.handle.net/2117/207202013-11-25T12:39:49ZNabona Francisco, NarcísnoBloom and Gallant formulation, Heuristics, Load matching, Medium-term power planningThe medium-term planning for a company participating in a pure pool market can be considered under different behavioral
principles: cartel, endogenous cartel with respect to hydro generation, and equilibrium. One of the important aspects of the model is the medium-term load matching, where load is represented by the load duration curves of each period into which the
medium-term horizon is subdivided.
The probabilistically exact Bloom and Gallant formulation of load matching could be employed, but its exponential number of
load-matching inequality constraints limits its use to small cases.
A heuristic has been proposed where only a small subset of the load-matching constraints, hopefully containing the active ones at the optimizer, is employed. This heuristic does not perform totally well with the more complex models, as those of endogenous cartel and equilibrium behavior, and a more comprehensive heuristic procedure is presented here. A well-known alternative method of medium-term load matching is the multi-block approximation to the load duration curve.
The three load-matching techniques mentioned above are here described and are compared, and computational results for realistic
systems are analyzed for the different behavioral models, showing the advantages of each procedure.A new optimal electricity market bid model solved through perspective cuts
http://hdl.handle.net/2117/18368
Title: A new optimal electricity market bid model solved through perspective cuts
Authors: Corchero García, Cristina; Mijangos Fernández, Eugenio; Heredia, F.-Javier (Francisco Javier)
Abstract: On current electricity markets the electrical utilities are faced with very sophisticated decision making problems under uncertainty. Moreover, when focusing in the short-term management, generation companies must include some medium-term products that directly influence their short-term strategies. In this work, the bilateral and physical futures contracts are included into the day-ahead market bid following MIBEL rules and a stochastic quadratic mixed-integer programming model is presented. The complexity of this stochastic programming problem makes unpractical the resolution of large-scale instances with general-purpose optimization codes. Therefore, in order to gain efficiency, a polyhedral outer approximation of the quadratic objective function obtained by means of perspective cuts (PC) is proposed. A set of instances of the problem has been defined with real data and solved with the PC methodology. The numerical results obtained show the efficiency of this methodology compared with standard mixed quadratic optimization solvers.Mon, 18 Mar 2013 12:33:44 GMThttp://hdl.handle.net/2117/183682013-03-18T12:33:44ZCorchero García, Cristina; Mijangos Fernández, Eugenio; Heredia, F.-Javier (Francisco Javier)noAMS90A AMS90B AMS90COn current electricity markets the electrical utilities are faced with very sophisticated decision making problems under uncertainty. Moreover, when focusing in the short-term management, generation companies must include some medium-term products that directly influence their short-term strategies. In this work, the bilateral and physical futures contracts are included into the day-ahead market bid following MIBEL rules and a stochastic quadratic mixed-integer programming model is presented. The complexity of this stochastic programming problem makes unpractical the resolution of large-scale instances with general-purpose optimization codes. Therefore, in order to gain efficiency, a polyhedral outer approximation of the quadratic objective function obtained by means of perspective cuts (PC) is proposed. A set of instances of the problem has been defined with real data and solved with the PC methodology. The numerical results obtained show the efficiency of this methodology compared with standard mixed quadratic optimization solvers.Optimum short-term hydrothermal scheduling with spinning reserve through network flows
http://hdl.handle.net/2117/15738
Title: Optimum short-term hydrothermal scheduling with spinning reserve through network flows
Authors: Heredia, F.-Javier (Francisco Javier); Nabona Francisco, Narcís
Abstract: Optimizing the thermal production of electricity in the short term in an integrated power system when a thermal unit commitment has been decided means coordinating hydro and thermal generation in order to obtain the minimum thermal generation costs over the time period under study. Fundamental constraints to be satisfied are the covering of each hourly load and satisfaction of spinning reserve requirements and transmission capacity limits. A nonlinear network flow model with linear side constraints with no decomposition into hydro and thermal subproblems was usedto solve and thermal scheduling. Hydrogeneration is linearized with respect to network variables and novel thermal generation and transmission network is introduced. Computational results are reported.Tue, 17 Apr 2012 17:31:33 GMThttp://hdl.handle.net/2117/157382012-04-17T17:31:33ZHeredia, F.-Javier (Francisco Javier); Nabona Francisco, NarcísnoOptimizing the thermal production of electricity in the short term in an integrated power system when a thermal unit commitment has been decided means coordinating hydro and thermal generation in order to obtain the minimum thermal generation costs over the time period under study. Fundamental constraints to be satisfied are the covering of each hourly load and satisfaction of spinning reserve requirements and transmission capacity limits. A nonlinear network flow model with linear side constraints with no decomposition into hydro and thermal subproblems was usedto solve and thermal scheduling. Hydrogeneration is linearized with respect to network variables and novel thermal generation and transmission network is introduced. Computational results are reported.An effective line search for the subgradient method
http://hdl.handle.net/2117/14709
Title: An effective line search for the subgradient method
Authors: Beltran, C; Heredia, F.-Javier (Francisco Javier)
Abstract: One of the main drawbacks of the subgradient method is
the tuning process to determine the sequence of steplengths. In this
paper, the radar subgradient method, a heuristic method designed
to compute a tuning-free subgradient steplength, is geometrically
motivated and algebraically deduced. The unit commitment problem,
which arises in the electrical engineering field, is used to compare the
performance of the subgradient method with the new radar subgradient
method.Fri, 20 Jan 2012 13:10:28 GMThttp://hdl.handle.net/2117/147092012-01-20T13:10:28ZBeltran, C; Heredia, F.-Javier (Francisco Javier)noOne of the main drawbacks of the subgradient method is
the tuning process to determine the sequence of steplengths. In this
paper, the radar subgradient method, a heuristic method designed
to compute a tuning-free subgradient steplength, is geometrically
motivated and algebraically deduced. The unit commitment problem,
which arises in the electrical engineering field, is used to compare the
performance of the subgradient method with the new radar subgradient
method.Unit commitment by augmented lagrangian relaxation: testing two decomposition approaches
http://hdl.handle.net/2117/14707
Title: Unit commitment by augmented lagrangian relaxation: testing two decomposition approaches
Authors: Beltrán Royo, César; Heredia, F.-Javier (Francisco Javier)
Abstract: One of the main drawbacks of the augmented Lagrangian
relaxation method is that the quadratic term introduced by the augmented
Lagrangian is not separable. We compare empirically and
theoretically two methods designed to cope with the nonseparability of
the Lagrangian function: the auxiliary problem principle method and
the block coordinated descent method. Also, we use the so-called unit
commitment problem to test both methods. The objective of the unit
commitment problem is to optimize the electricity production and distribution,
considering a short-term planning horizon.Fri, 20 Jan 2012 13:06:17 GMThttp://hdl.handle.net/2117/147072012-01-20T13:06:17ZBeltrán Royo, César; Heredia, F.-Javier (Francisco Javier)noOne of the main drawbacks of the augmented Lagrangian
relaxation method is that the quadratic term introduced by the augmented
Lagrangian is not separable. We compare empirically and
theoretically two methods designed to cope with the nonseparability of
the Lagrangian function: the auxiliary problem principle method and
the block coordinated descent method. Also, we use the so-called unit
commitment problem to test both methods. The objective of the unit
commitment problem is to optimize the electricity production and distribution,
considering a short-term planning horizon.Quadratic regularizations in an interior-point method for primal block-angular problems
http://hdl.handle.net/2117/14412
Title: Quadratic regularizations in an interior-point method for primal block-angular problems
Authors: Castro Pérez, Jordi; Cuesta Andrea, Jordi
Abstract: One of the most efficient interior-point methods for some classes of primal block-angular problems solves the normal equations by a combination of Cholesky factorizations and preconditioned conjugate gradient for, respectively, the block and linking constraints. Its efficiency depends on the spectral radius—in [0,1)— of a certain matrix in the definition of the preconditioner. Spectral radius close to 1 degrade the performance of the approach. The purpose of this work is twofold. First, to show that a separable quadratic regularization term in the objective reduces the spectral radius, significantly improving the overall performance in some classes of instances. Second, to consider a regularization term which decreases with the barrier function, thus with no need for an extra parameter. Computational experience with some primal block-angular problems confirms the efficiency of the regularized approach. In particular, for some difficult problems, the solution time is reduced by a factor of two to ten by the regularization term, outperforming state-of-the-art commercial solvers.Thu, 05 Jan 2012 08:42:28 GMThttp://hdl.handle.net/2117/144122012-01-05T08:42:28ZCastro Pérez, Jordi; Cuesta Andrea, JordinoOne of the most efficient interior-point methods for some classes of primal block-angular problems solves the normal equations by a combination of Cholesky factorizations and preconditioned conjugate gradient for, respectively, the block and linking constraints. Its efficiency depends on the spectral radius—in [0,1)— of a certain matrix in the definition of the preconditioner. Spectral radius close to 1 degrade the performance of the approach. The purpose of this work is twofold. First, to show that a separable quadratic regularization term in the objective reduces the spectral radius, significantly improving the overall performance in some classes of instances. Second, to consider a regularization term which decreases with the barrier function, thus with no need for an extra parameter. Computational experience with some primal block-angular problems confirms the efficiency of the regularized approach. In particular, for some difficult problems, the solution time is reduced by a factor of two to ten by the regularization term, outperforming state-of-the-art commercial solvers.A stochastic programming model for the thermal optimal day-ahead bid problem with physical futures contracts
http://hdl.handle.net/2117/14109
Title: A stochastic programming model for the thermal optimal day-ahead bid problem with physical futures contracts
Authors: Corchero García, Cristina; Heredia, F.-Javier (Francisco Javier)
Abstract: The reorganization of the electricity industry in Spain completed a new step with the start-up of the Derivatives Market.
One main characteristic of MIBEL’s Derivatives Market is the existence of physical futures contracts; they imply
the obligation to physically settle the energy. The market regulation establishes the mechanism for including those
physical futures in the day-ahead bidding of the Generation Companies. The goal of this work is to optimize coordination
between physical futures contracts and the day-ahead bidding which follow this regulation. We propose a
stochastic quadratic mixed-integer programming model which maximizes the expected profits, taking into account futures
contracts settlement. The model gives the simultaneous optimization for the Day-Ahead Market bidding strategy
and power planning production (unit commitment) for the thermal units of a price-taker Generation Company. The
uncertainty of the Day-Ahead Market price is included in the stochastic model through a set of scenarios. Implementation
details and some first computational experiences for small real cases are presented.Tue, 29 Nov 2011 13:01:40 GMThttp://hdl.handle.net/2117/141092011-11-29T13:01:40ZCorchero García, Cristina; Heredia, F.-Javier (Francisco Javier)noThe reorganization of the electricity industry in Spain completed a new step with the start-up of the Derivatives Market.
One main characteristic of MIBEL’s Derivatives Market is the existence of physical futures contracts; they imply
the obligation to physically settle the energy. The market regulation establishes the mechanism for including those
physical futures in the day-ahead bidding of the Generation Companies. The goal of this work is to optimize coordination
between physical futures contracts and the day-ahead bidding which follow this regulation. We propose a
stochastic quadratic mixed-integer programming model which maximizes the expected profits, taking into account futures
contracts settlement. The model gives the simultaneous optimization for the Day-Ahead Market bidding strategy
and power planning production (unit commitment) for the thermal units of a price-taker Generation Company. The
uncertainty of the Day-Ahead Market price is included in the stochastic model through a set of scenarios. Implementation
details and some first computational experiences for small real cases are presented.Software libre: una oportunidad para los investigadores
http://hdl.handle.net/2117/11744
Title: Software libre: una oportunidad para los investigadores
Authors: Rius Carrasco, Roser; González Alastrué, José Antonio
Abstract: El mundo del software está viviendo en el último decenio una peculiar revolución que tiene como principal actor el software
libre. El fenómeno es de tal magnitud que recientemente Mark Driver, de la acreditada consultora Gartner, predecı´a que, en un par
de an˜os, el 80% del software comercial contendrá módulos de software libre1.
En el presente número, el artículo de Seoane et al2 hace una apuesta definitiva por el uso de software libre en el a´mbito clínico.
No se limita a ello, en realidad: lanza el reto a toda la comunidad e invita a cuestionarse « ¿Debo seguir ligado a un costoso software propietario? ¿Debo denunciar a quien usa ilegalmente este
software?».Wed, 09 Mar 2011 10:42:29 GMThttp://hdl.handle.net/2117/117442011-03-09T10:42:29ZRius Carrasco, Roser; González Alastrué, José AntonionoEl mundo del software está viviendo en el último decenio una peculiar revolución que tiene como principal actor el software
libre. El fenómeno es de tal magnitud que recientemente Mark Driver, de la acreditada consultora Gartner, predecı´a que, en un par
de an˜os, el 80% del software comercial contendrá módulos de software libre1.
En el presente número, el artículo de Seoane et al2 hace una apuesta definitiva por el uso de software libre en el a´mbito clínico.
No se limita a ello, en realidad: lanza el reto a toda la comunidad e invita a cuestionarse « ¿Debo seguir ligado a un costoso software propietario? ¿Debo denunciar a quien usa ilegalmente este
software?».A Parameter-free approach for solving combinatorial optimization problems through biased randomization of efficient heuristics
http://hdl.handle.net/2117/11227
Title: A Parameter-free approach for solving combinatorial optimization problems through biased randomization of efficient heuristics
Authors: Ionescu, Dragos; Juan Pérez, Angel Alejandro; Faulín, Javier; Ferrer Biosca, Alberto
Abstract: This paper discusses the use of probabilistic or randomized algorithms for solving combinatorial optimization problems. Our approach employs non-uniform probability distributions to add a biased random behavior to classical heuristics so a large set of alternative good solutions can be quickly obtained in a natural way and without complex conguration processes. This procedure is especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods, both of exact and approximate nature, may fail to reach their full potential. The results obtained are promising enough to suggest that randomizing classical heuristics is a powerful
method that can be successfully applied in a variety of casesMon, 31 Jan 2011 09:43:37 GMThttp://hdl.handle.net/2117/112272011-01-31T09:43:37ZIonescu, Dragos; Juan Pérez, Angel Alejandro; Faulín, Javier; Ferrer Biosca, AlbertonoThis paper discusses the use of probabilistic or randomized algorithms for solving combinatorial optimization problems. Our approach employs non-uniform probability distributions to add a biased random behavior to classical heuristics so a large set of alternative good solutions can be quickly obtained in a natural way and without complex conguration processes. This procedure is especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods, both of exact and approximate nature, may fail to reach their full potential. The results obtained are promising enough to suggest that randomizing classical heuristics is a powerful
method that can be successfully applied in a variety of casesShort-term hydrothermal coordination by augmented lagrangean relaxation: a new multiplier updating
http://hdl.handle.net/2117/10883
Title: Short-term hydrothermal coordination by augmented lagrangean relaxation: a new multiplier updating
Authors: Beltrán Royo, César; Heredia, F.-Javier (Francisco Javier)
Abstract: The Augmented Lagrangean Relaxation (ALR) method is one of the most powerful techniques
to solve the Short-Term Hydrothermal Coordination (STHC) problem. A crucial step when
using the ALR method is the updating of the multipliers. In this paper we present a new
multiplier updating procedure: the Gradient with Radar Step (GRS) method. The method
has been successfully tested by solving medium-scale examples of the STHC problemMon, 03 Jan 2011 14:04:10 GMThttp://hdl.handle.net/2117/108832011-01-03T14:04:10ZBeltrán Royo, César; Heredia, F.-Javier (Francisco Javier)noThe Augmented Lagrangean Relaxation (ALR) method is one of the most powerful techniques
to solve the Short-Term Hydrothermal Coordination (STHC) problem. A crucial step when
using the ALR method is the updating of the multipliers. In this paper we present a new
multiplier updating procedure: the Gradient with Radar Step (GRS) method. The method
has been successfully tested by solving medium-scale examples of the STHC problem