Carlos Cruz Corona | Universidad de Granada (original) (raw)
Papers by Carlos Cruz Corona
Applied Computational Intelligence and Soft Computing, 2015
In real world, many optimization problems are dynamic, which means that their model elements vary... more In real world, many optimization problems are dynamic, which means that their model elements vary with time. These problems have received increasing attention over time, especially from the viewpoint of metaheuristics methods. In this context, experimentation is a crucial task because of the stochastic nature of both algorithms and problems. Currently, there are several technologies whose methods, problems, and performance measures can be implemented. However, in most of them, certain features that make the experimentation process easy are not present. Examples of such features are the statistical analysis of the results and a graphical user interface (GUI) that allows an easy management of the experimentation process. Bearing in mind these limitations, in the present work, we present DynOptLab, a software tool for experimental analysis in dynamic environments. DynOptLab has two main components: (1) an object-oriented framework to facilitate the implementation of new proposals and (...
Studies in Fuzziness and Soft Computing, 2010
Optimization is a procedure of finding and comparing feasible solutions until no better solution ... more Optimization is a procedure of finding and comparing feasible solutions until no better solution can be found. It can be divided into several fields, one of which is the Convex Optimization. It is characterized by a convex objective function and convex constraint functions over a convex set which is the set of the decision variables. This can be viewed, on the one hand, as a particular case of nonlinear programming and, on the other hand, as a general case of linear programming. Convex optimization has applications in a wide range of real-world applications, whose data often cannot be formulate precisely. Hence it makes perfect sense to apply fuzzy set theory as a way to mathematically describe this vagueness. In this paper we review the theory about this topic and describe some flexible and possibilistic programming models to solve fuzzy convex programming problems. Flexible programming uses fuzzy sets to represent the vagueness of the decision maker's aspirations and constraints, while possibilistic programming models imprecise or ambiguous data by possibility distributions.
Informática y Sistemas: Revista de Tecnologías de la Informática y las Comunicaciones
En la actualidad, los aparatos de medición que trabajan con las señales de oximetría solo son cap... more En la actualidad, los aparatos de medición que trabajan con las señales de oximetría solo son capaces de obtener el ritmo cardíaco y el porcentaje de saturación de oxígeno en sangre. Los hospitales utilizan estos aparatos para realizar seguimientos a pacientes, donde es primordial la vigilancia de la salud de estos pacientes. Este tipo de señales posee más características, las cuales podrían ayudar mucho más en este sector. Al ser ondas que recorren las arterias de todo el sistema del cuerpo humano, aportan datos que los dispositivos existentes no analizan. En este trabajo se realiza un estudio de la oximetría, la medición realizada y las señales obtenidas. Además se hace una investigación acerca de la señal de fotoplestimografía con el objetivo de examinar las características que la definan así como su proceso de obtención, filtrado y extracción de datos. Como resultado de este trabajo se consigue un sistema mejorado de monitorización que obtiene todas las características de la señ...
International Journal of Bio-Inspired Computation, 2016
Self-adaptation is a popular parameter control technique in evolutionary computation, which has b... more Self-adaptation is a popular parameter control technique in evolutionary computation, which has been extensively studied in stationary optimisation. In the context of dynamic optimisation problems (DOPs), there are research works that suggest the application of such technique. Nevertheless, some important issues remain open, for example, how self-adaptation can be more profitable for a given algorithm. From the survey we made, it is possible to distinguish three main application levels of self-adaptation in dynamic environments: metaheuristic level, 'mechanism for DOPs' level, and the combination of both. While most of the related works belong to the first level, a small number can be grouped in the second one. However, in contrast to previous two, unfortunately, very little or nothing has been done with the third one. Based on these motivations, in this paper we empirically analysed the role of several self-adaptive models in these levels using multipopulation differential evolution algorithms as baseline. The results suggest that self-adaptation has a significant impact when applied at least to the 'mechanism for DOPs' level.
Studies in Computational Intelligence, 2014
Regression analysis, which includes any techniques for modeling and analyzing several variables, ... more Regression analysis, which includes any techniques for modeling and analyzing several variables, is a statistical tool that focuses in finding a relationship between a dependent variable and one or more independent variables. When this relationship is found, some values of parameters are determined which help a function to best fit in a set of data observations. In regression analysis, it is also interesting to characterize the variation of the depend variable around the independent ones. A regression problem can be formulated as a mathematical programming problem, where the objective is to minimize the difference between the estimated values and the observed values. This proposal provides a fuzzy solution to the problem that involves all particular -punctual- solutions provided by other methods. To clarify the above developments, a numerical example about the price mechanism of prefabricated houses is analyzed.
INTELIGENCIA ARTIFICIAL, 2007
Advances in Environmental Engineering and Green Technologies, 2015
This book is published in the IGI Global book series Advances in Environmental Engineering and Gr... more This book is published in the IGI Global book series Advances in Environmental Engineering and Green Technologies (AEEGT)
Soft Computing, 2013
The present work proposes a simple but effective self-adaptive strategy to control the behaviour ... more The present work proposes a simple but effective self-adaptive strategy to control the behaviour of a differential evolution (DE) based multipopulation algorithm for dynamic environments. Specifically, the proposed scheme is aimed to control the creation of random individuals by the self-adaptation of the involved parameter. An interaction scheme between random and conventional DE individuals is also proposed and analyzed. The conducted computational experiments show that self-adaptation is profitable, leading to an algorithm that is as competitive as other efficient methods and able to beat the winner of the CEC 2009 competition on dynamic environments.
Memetic Computing, 2011
Particle swarm optimization has been successfully applied in many research and application areas ... more Particle swarm optimization has been successfully applied in many research and application areas because of its effectiveness and easy implementation. In this work we extend one of its variants to address multi-modal dynamic optimization problems, the multi-swarm PSO (mPSO) proposed by Blackwell and Branke. The aim of our proposal is to increase the efficiency of this algorithm. To this end, we propose techniques operating at swarm level: one of which divides each swarm into two groups depending on the quality of the particles for facing the loss of diversity, and the other control the number of active swarms during the run using a fuzzy rule. A detailed experimental analysis shows the robustness of our proposal. Keywords Multi-swarm PSO • Dynamic environments • Fuzzy rule 1 Introduction Modern society is full of complex processes that require new techniques for their analysis and treatment. In many cases, this complexity is due to the dynamism of variables, rules or objectives involved. Several examples of dynamic problems can be given: optimal allocation of resources to
International Journal of Intelligent Systems, 2009
In recent years, biological and natural processes have been increasingly influencing methodologie... more In recent years, biological and natural processes have been increasingly influencing methodologies in science and technology. In particular, the role played by the cooperation among individuals is being studied more frequently and profoundly in diverse areas of knowledge. We present here a multiagent decentralized strategy for dynamic optimization problems where a population of cooperative agents and solutions are used to deal with the moving peaks problem. We focus on cooperation and diversity mechanisms, and we study how different alternatives affect the performance of the strategy.
Many complex real-world optimization problems are dynamic. In order to approach them is necessary... more Many complex real-world optimization problems are dynamic. In order to approach them is necessary to have tools that are able to adapt to the changes that take place in the time. In this work we propose a strategy that jointly use a set of solutions and a set of simple agents. Implicit and explicit memory mechanisms are used and we analyze the behavior of the strategy when coupled with a fuzzy rule to control the updating of the solution's set. Tests are performed on the moving peaks benchmark problem under different scenarios.
Studies in Fuzziness and Soft Computing, 2010
ABSTRACT Optimization is a procedure of finding and comparing feasible solutions until no better ... more ABSTRACT Optimization is a procedure of finding and comparing feasible solutions until no better solution can be found. It can be divided into several fields, one of which is the Convex Optimization. It is characterized by a convex objective function and convex constraint functions over a convex set which is the set of the decision variables. This can be viewed, on the one hand, as a particular case of nonlinear programming and, on the other hand, as a general case of linear programming. Convex optimization has applications in a wide range of real-world applications, whose data often cannot be formulate precisely. Hence it makes perfect sense to apply fuzzy set theory as a way to mathematically describe this vagueness. In this paper we review the theory about this topic and describe some flexible and possibilistic programming models to solve fuzzy convex programming problems. Flexible programming uses fuzzy sets to represent the vagueness of the decision maker’s aspirations and constraints, while possibilistic programming models imprecise or ambiguous data by possibility distributions.
Several decision scenarios can be modeled as dynamic optimization problems (DOPs), which have bee... more Several decision scenarios can be modeled as dynamic optimization problems (DOPs), which have been tackled by meta-heuristics techniques over the last years. However, so far, most related works assume that changes occur every equal time periods, which may be rather idealistic in real-world scenarios. In contrast, DOPs with variable change frequency (DOPVCFs) impose as main challenge to the algorithm: how to adapt to different environments during the run, as fast as possible. In that sense, self-adaptation is parameter control technique with remarkable success in complex scenarios. So, in the present work we aim to analyze the impact of self-adaptation in solving DOPVCFs. To achieve this, we have designed an experimental study by considering a recently proposed self-adaptive technique over several test scenarios. The results confirm that self-adaptation has not only a positive, but also significant impact in the algorithm performance.
The present work proposes a simple but effective self-adaptive strategy to control the behaviour ... more The present work proposes a simple but effective self-adaptive strategy to control the behaviour of a differential evolution (DE) based multipopulation algorithm for dynamic environments. Specifically, the proposed scheme is aimed to control the creation of random individuals by the self-adaptation of the involved parameter. An interaction scheme between random and conventional DE individuals is also proposed and analyzed. The conducted computational experiments show that self-adaptation is profitable, leading to an algorithm that is as competitive as other efficient methods and able to beat the winner of the CEC 2009 competition on dynamic environments.
Fuzzy Optimization and Decision Making, 2013
ABSTRACT Although quadratic programming problems are a special class of nonlinear programming, th... more ABSTRACT Although quadratic programming problems are a special class of nonlinear programming, they can also be seen as general linear programming problems. These quadratic problems are of the utmost importance in an increasing variety of practical fields. As, in addition, ambiguity and vagueness are natural and ever-present in real-life situations requiring operative solutions, it makes perfect sense to address them using fuzzy concepts formulated as quadratic programming problems with uncertainty, i.e., as Fuzzy Quadratic Programming problems. This work proposes two novel fuzzy-sets-based methods to solve a particular class of Fuzzy Quadratic Programming problems which have vagueness coefficients in the objective function. Moreover, two other linear approaches are extended to solve the quadratic case. Finally, it is shown that the solutions reached from the extended approaches may be obtained from two proposed parametric multiobjective approaches.
In recent years, biological and natural processes have been increasingly influencing methodologie... more In recent years, biological and natural processes have been increasingly influencing methodologies in science and technology. In particular, the role played by the cooperation among individuals is being studied more frequently and profoundly in diverse areas of knowledge. We present here a multiagent decentralized strategy for dynamic optimization problems where a population of cooperative agents and solutions are used to deal with the moving peaks problem. We focus on cooperation and diversity mechanisms, and we study how different alternatives affect the performance of the strategy. C 2009 Wiley Periodicals, Inc.
Applied Computational Intelligence and Soft Computing, 2015
In real world, many optimization problems are dynamic, which means that their model elements vary... more In real world, many optimization problems are dynamic, which means that their model elements vary with time. These problems have received increasing attention over time, especially from the viewpoint of metaheuristics methods. In this context, experimentation is a crucial task because of the stochastic nature of both algorithms and problems. Currently, there are several technologies whose methods, problems, and performance measures can be implemented. However, in most of them, certain features that make the experimentation process easy are not present. Examples of such features are the statistical analysis of the results and a graphical user interface (GUI) that allows an easy management of the experimentation process. Bearing in mind these limitations, in the present work, we present DynOptLab, a software tool for experimental analysis in dynamic environments. DynOptLab has two main components: (1) an object-oriented framework to facilitate the implementation of new proposals and (...
Studies in Fuzziness and Soft Computing, 2010
Optimization is a procedure of finding and comparing feasible solutions until no better solution ... more Optimization is a procedure of finding and comparing feasible solutions until no better solution can be found. It can be divided into several fields, one of which is the Convex Optimization. It is characterized by a convex objective function and convex constraint functions over a convex set which is the set of the decision variables. This can be viewed, on the one hand, as a particular case of nonlinear programming and, on the other hand, as a general case of linear programming. Convex optimization has applications in a wide range of real-world applications, whose data often cannot be formulate precisely. Hence it makes perfect sense to apply fuzzy set theory as a way to mathematically describe this vagueness. In this paper we review the theory about this topic and describe some flexible and possibilistic programming models to solve fuzzy convex programming problems. Flexible programming uses fuzzy sets to represent the vagueness of the decision maker's aspirations and constraints, while possibilistic programming models imprecise or ambiguous data by possibility distributions.
Informática y Sistemas: Revista de Tecnologías de la Informática y las Comunicaciones
En la actualidad, los aparatos de medición que trabajan con las señales de oximetría solo son cap... more En la actualidad, los aparatos de medición que trabajan con las señales de oximetría solo son capaces de obtener el ritmo cardíaco y el porcentaje de saturación de oxígeno en sangre. Los hospitales utilizan estos aparatos para realizar seguimientos a pacientes, donde es primordial la vigilancia de la salud de estos pacientes. Este tipo de señales posee más características, las cuales podrían ayudar mucho más en este sector. Al ser ondas que recorren las arterias de todo el sistema del cuerpo humano, aportan datos que los dispositivos existentes no analizan. En este trabajo se realiza un estudio de la oximetría, la medición realizada y las señales obtenidas. Además se hace una investigación acerca de la señal de fotoplestimografía con el objetivo de examinar las características que la definan así como su proceso de obtención, filtrado y extracción de datos. Como resultado de este trabajo se consigue un sistema mejorado de monitorización que obtiene todas las características de la señ...
International Journal of Bio-Inspired Computation, 2016
Self-adaptation is a popular parameter control technique in evolutionary computation, which has b... more Self-adaptation is a popular parameter control technique in evolutionary computation, which has been extensively studied in stationary optimisation. In the context of dynamic optimisation problems (DOPs), there are research works that suggest the application of such technique. Nevertheless, some important issues remain open, for example, how self-adaptation can be more profitable for a given algorithm. From the survey we made, it is possible to distinguish three main application levels of self-adaptation in dynamic environments: metaheuristic level, 'mechanism for DOPs' level, and the combination of both. While most of the related works belong to the first level, a small number can be grouped in the second one. However, in contrast to previous two, unfortunately, very little or nothing has been done with the third one. Based on these motivations, in this paper we empirically analysed the role of several self-adaptive models in these levels using multipopulation differential evolution algorithms as baseline. The results suggest that self-adaptation has a significant impact when applied at least to the 'mechanism for DOPs' level.
Studies in Computational Intelligence, 2014
Regression analysis, which includes any techniques for modeling and analyzing several variables, ... more Regression analysis, which includes any techniques for modeling and analyzing several variables, is a statistical tool that focuses in finding a relationship between a dependent variable and one or more independent variables. When this relationship is found, some values of parameters are determined which help a function to best fit in a set of data observations. In regression analysis, it is also interesting to characterize the variation of the depend variable around the independent ones. A regression problem can be formulated as a mathematical programming problem, where the objective is to minimize the difference between the estimated values and the observed values. This proposal provides a fuzzy solution to the problem that involves all particular -punctual- solutions provided by other methods. To clarify the above developments, a numerical example about the price mechanism of prefabricated houses is analyzed.
INTELIGENCIA ARTIFICIAL, 2007
Advances in Environmental Engineering and Green Technologies, 2015
This book is published in the IGI Global book series Advances in Environmental Engineering and Gr... more This book is published in the IGI Global book series Advances in Environmental Engineering and Green Technologies (AEEGT)
Soft Computing, 2013
The present work proposes a simple but effective self-adaptive strategy to control the behaviour ... more The present work proposes a simple but effective self-adaptive strategy to control the behaviour of a differential evolution (DE) based multipopulation algorithm for dynamic environments. Specifically, the proposed scheme is aimed to control the creation of random individuals by the self-adaptation of the involved parameter. An interaction scheme between random and conventional DE individuals is also proposed and analyzed. The conducted computational experiments show that self-adaptation is profitable, leading to an algorithm that is as competitive as other efficient methods and able to beat the winner of the CEC 2009 competition on dynamic environments.
Memetic Computing, 2011
Particle swarm optimization has been successfully applied in many research and application areas ... more Particle swarm optimization has been successfully applied in many research and application areas because of its effectiveness and easy implementation. In this work we extend one of its variants to address multi-modal dynamic optimization problems, the multi-swarm PSO (mPSO) proposed by Blackwell and Branke. The aim of our proposal is to increase the efficiency of this algorithm. To this end, we propose techniques operating at swarm level: one of which divides each swarm into two groups depending on the quality of the particles for facing the loss of diversity, and the other control the number of active swarms during the run using a fuzzy rule. A detailed experimental analysis shows the robustness of our proposal. Keywords Multi-swarm PSO • Dynamic environments • Fuzzy rule 1 Introduction Modern society is full of complex processes that require new techniques for their analysis and treatment. In many cases, this complexity is due to the dynamism of variables, rules or objectives involved. Several examples of dynamic problems can be given: optimal allocation of resources to
International Journal of Intelligent Systems, 2009
In recent years, biological and natural processes have been increasingly influencing methodologie... more In recent years, biological and natural processes have been increasingly influencing methodologies in science and technology. In particular, the role played by the cooperation among individuals is being studied more frequently and profoundly in diverse areas of knowledge. We present here a multiagent decentralized strategy for dynamic optimization problems where a population of cooperative agents and solutions are used to deal with the moving peaks problem. We focus on cooperation and diversity mechanisms, and we study how different alternatives affect the performance of the strategy.
Many complex real-world optimization problems are dynamic. In order to approach them is necessary... more Many complex real-world optimization problems are dynamic. In order to approach them is necessary to have tools that are able to adapt to the changes that take place in the time. In this work we propose a strategy that jointly use a set of solutions and a set of simple agents. Implicit and explicit memory mechanisms are used and we analyze the behavior of the strategy when coupled with a fuzzy rule to control the updating of the solution's set. Tests are performed on the moving peaks benchmark problem under different scenarios.
Studies in Fuzziness and Soft Computing, 2010
ABSTRACT Optimization is a procedure of finding and comparing feasible solutions until no better ... more ABSTRACT Optimization is a procedure of finding and comparing feasible solutions until no better solution can be found. It can be divided into several fields, one of which is the Convex Optimization. It is characterized by a convex objective function and convex constraint functions over a convex set which is the set of the decision variables. This can be viewed, on the one hand, as a particular case of nonlinear programming and, on the other hand, as a general case of linear programming. Convex optimization has applications in a wide range of real-world applications, whose data often cannot be formulate precisely. Hence it makes perfect sense to apply fuzzy set theory as a way to mathematically describe this vagueness. In this paper we review the theory about this topic and describe some flexible and possibilistic programming models to solve fuzzy convex programming problems. Flexible programming uses fuzzy sets to represent the vagueness of the decision maker’s aspirations and constraints, while possibilistic programming models imprecise or ambiguous data by possibility distributions.
Several decision scenarios can be modeled as dynamic optimization problems (DOPs), which have bee... more Several decision scenarios can be modeled as dynamic optimization problems (DOPs), which have been tackled by meta-heuristics techniques over the last years. However, so far, most related works assume that changes occur every equal time periods, which may be rather idealistic in real-world scenarios. In contrast, DOPs with variable change frequency (DOPVCFs) impose as main challenge to the algorithm: how to adapt to different environments during the run, as fast as possible. In that sense, self-adaptation is parameter control technique with remarkable success in complex scenarios. So, in the present work we aim to analyze the impact of self-adaptation in solving DOPVCFs. To achieve this, we have designed an experimental study by considering a recently proposed self-adaptive technique over several test scenarios. The results confirm that self-adaptation has not only a positive, but also significant impact in the algorithm performance.
The present work proposes a simple but effective self-adaptive strategy to control the behaviour ... more The present work proposes a simple but effective self-adaptive strategy to control the behaviour of a differential evolution (DE) based multipopulation algorithm for dynamic environments. Specifically, the proposed scheme is aimed to control the creation of random individuals by the self-adaptation of the involved parameter. An interaction scheme between random and conventional DE individuals is also proposed and analyzed. The conducted computational experiments show that self-adaptation is profitable, leading to an algorithm that is as competitive as other efficient methods and able to beat the winner of the CEC 2009 competition on dynamic environments.
Fuzzy Optimization and Decision Making, 2013
ABSTRACT Although quadratic programming problems are a special class of nonlinear programming, th... more ABSTRACT Although quadratic programming problems are a special class of nonlinear programming, they can also be seen as general linear programming problems. These quadratic problems are of the utmost importance in an increasing variety of practical fields. As, in addition, ambiguity and vagueness are natural and ever-present in real-life situations requiring operative solutions, it makes perfect sense to address them using fuzzy concepts formulated as quadratic programming problems with uncertainty, i.e., as Fuzzy Quadratic Programming problems. This work proposes two novel fuzzy-sets-based methods to solve a particular class of Fuzzy Quadratic Programming problems which have vagueness coefficients in the objective function. Moreover, two other linear approaches are extended to solve the quadratic case. Finally, it is shown that the solutions reached from the extended approaches may be obtained from two proposed parametric multiobjective approaches.
In recent years, biological and natural processes have been increasingly influencing methodologie... more In recent years, biological and natural processes have been increasingly influencing methodologies in science and technology. In particular, the role played by the cooperation among individuals is being studied more frequently and profoundly in diverse areas of knowledge. We present here a multiagent decentralized strategy for dynamic optimization problems where a population of cooperative agents and solutions are used to deal with the moving peaks problem. We focus on cooperation and diversity mechanisms, and we study how different alternatives affect the performance of the strategy. C 2009 Wiley Periodicals, Inc.
Studies in Computational Intelligence (Series), 2009
... Strategies..... 53 Lıdice Camps Echevarrıa, Orestes Llanes-Santiago, Antonio Jose da Silva N... more ... Strategies..... 53 Lıdice Camps Echevarrıa, Orestes Llanes-Santiago, Antonio Jose da Silva Neto A New Metaheuristic Bat-Inspired Algorithm..... 65 Xin-She Yang Evaluation of a Catalytic Search Algorithm..... ...
Many complex real-world optimization problems are dynamic. In order to approach them is necessary... more Many complex real-world optimization problems are dynamic. In order to approach them is necessary to have tools that are able to adapt to the changes that take place in the time. In this work we propose a strategy that jointly use a set of solutions and a set of simple agents. Implicit and explicit memory mechanisms are used and we analyze the behavior of the strategy when coupled with a fuzzy rule to control the updating of the solution's set. Tests are performed on the moving peaks benchmark problem under different scenarios.
The Truck and Trailer Routing Problem, TTRP, is an extension of the well-known Vehicle Routing Pr... more The Truck and Trailer Routing Problem, TTRP, is an extension of the well-known Vehicle Routing Problem. This problem consist of a heterogeneous fleet composed of trucks and trailers to serve a set of customers. Most of models used in the literature assume that the data available are accurate, but in many practical problems the available knowledge about some data and parameters of the model involving uncertainty. In this study, we propose the application of a Soft Computing-based method to model and solve TTRP when the set of constrains is imprecise.
Lecture Notes in Computer Science, 2008
The use of Semantic Web Technologies in E-learning has turned to be more and more significant in ... more The use of Semantic Web Technologies in E-learning has turned to be more and more significant in the last years. In this paper, an approach that makes use of semantic web technologies to support the assessment of open questions in e-learning courses is described. The knowledge of the course is represented by means of a domain ontology, which is used
Quadratic programming can be seen both as a general approach to linear programming and a special ... more Quadratic programming can be seen both as a general approach to linear programming and a special class of nonlinear programming. Moreover, Quadratic Programming problems are of utmost importance in a variety of relevant practical fields, such as, portfolio selection. This work presents and develops a novel fuzzy-sets-based method that solves a class of quadratic programming problems with vagueness in the set of constraints. As vagueness is natural and ever-present in real-life situations requiring solutions, it makes perfect sense to attempt to address them using fuzzy quadratic programming. This kind of problem modeling is being applied in an increasing variety of practical fields especially those with logistics problems. Some illustrative numerical examples illustrating the solution approach are solved and analyzed to show the efficiency of this proposed method.
Feedback is an important component of assessment in learning environments, because it allows stud... more Feedback is an important component of assessment in learning environments, because it allows students to know their learning flaws, and feedback information is also useful for teachers to design learning contents adapted to the needs of the students. Therefore, the availability of feedback constitutes a new learning opportunity. In this paper we describe an approach based on Semantic Web technologies for generating useful semantic feedback for both teachers and students.
Fuzzy applications in industrial engineering
This paper makes a deeper study of a multi-thread based cooperative strategy, previously proposed... more This paper makes a deeper study of a multi-thread based cooperative strategy, previously proposed by us, to solve combinatorial optimization problems. In this strategy, each thread stands for a different optimization algorithm (or the same one with different settings) and they are all controlled by a coordinator. Both, the solvers threads and the coordinator thread have been modeled by soft computing techniques. We evaluate the performance of the strategy according to the number of threads using instances of the knapsack problem.
Encyclopedia of Artificial Intelligence, 2009
Working on artificial intelligence, one of the tasks we can carry on is optimization of the possi... more Working on artificial intelligence, one of the tasks we can carry on is optimization of the possible solutions of a problem. Optimization problems appear. In optimization problems we search for the best solution, or one good enough, to a problem among a lot of alternatives. Problems we try to solve are usual in daily living. Every person constantly works out optimization problems, e.g. finding the quickest way from home to work taking into account traffic restrictions. Humans can find efficiently solutions to these problems because these are easy enough. Nevertheless, problems can be more complex, for example reducing fuel consumption of a fleet of plains. Computational algorithms are required to tackle this kind of problems. A first approach to solve them is using an exhaustive search. Theoretically, this method always finds the solution, but is not efficient as its execution time grows exponentially. In order to improve this method heuristics were proposed. Heuristics are intelligent techniques, methods or procedures that use expert knowledge to solve tasks; they try to obtain a high performance referring to solution quality and used resources. Metaheuristics, term first used by Fred Glover in 1986 (Glover, 1986), arise to improve heuristics, and can be defined as (Melián, Moreno & Moreno, 2003) ‘intelligent strategies for designing and improving very general heuristic procedures with a high performance’. Since Glover the field has been extensively developed. The current trend is designing new metaheuristics that improve the solution to given problems. However, another line, very interesting, is reuse existing metaheuristics in a coordinated system. In this article we present two different methods following this line.
A cooperative multi thread strategy based on fuzzy sets and systems ideas
Fuzzy Optimization: Recent Advances and Applications, 2010
Optimization is a procedure of finding and comparing feasible solutions until no better solution ... more Optimization is a procedure of finding and comparing feasible solutions until no better solution can be found. It can be divided into several fields, one of which is the Convex Optimization. It is characterized by a convex objective function and convex constraint functions over a convex set which is the set of the decision variables. This can be viewed, on the one hand, as a particular case of nonlinear programming and, on the other hand, as a general case of linear programming. Convex optimization has applications in a wide range of real-world applications, whose data often cannot be formulate precisely. Hence it makes perfect sense to apply fuzzy set theory as a way to mathematically describe this vagueness. In this paper we review the theory about this topic and describe some flexible and possibilistic programming models to solve fuzzy convex programming problems. Flexible programming uses fuzzy sets to represent the vagueness of the decision maker’s aspirations and constraints, while possibilistic programming models imprecise or ambiguous data by possibility distributions.
Encyclopedia of Networked and Virtual Organizations, 2008
NP-complete problems, like many of those arising in industry, cannot be approached with exact too... more NP-complete problems, like many of those arising in industry, cannot be approached with exact tools in reasonable time, so, approximation approaches are required. Among such approaches, heuristics and metaheuristics methods are considered as very useful tools to obtain reasonably good solutions in limited time for such complex problems, but their application is far from trivial.
International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems
Recently, the Truck and Trailer Routing Problem (TTRP) has been tackled with uncertainty in the c... more Recently, the Truck and Trailer Routing Problem (TTRP) has been tackled with uncertainty in the coefficients of constrains. In order to solve this problem it is necessary to use methods for comparison fuzzy numbers. The problem of ordering fuzzy quantities has been addressed by many authors and there are many indices to perform this task. However, it is impossible to give a final answer to the question on what ranking method is the best in this problem. In this paper we focus our attention on a model to characterize TTRP instances. We use a data mining algorithm to derive a decision tree that determined the best method for comparison based on the characteristics of the TTRP problem to be solved.
Fuzzy Logic in Its 50th Year: New Developments, Directions and Challenges, 2016
The Truck and Trailer Routing Problem uses trucks pulling trailers as a distinctive feature of th... more The Truck and Trailer Routing Problem uses trucks pulling trailers as a distinctive feature of the Vehicle Routing Problem. Recently, this problem has been treated considering the capacity constraints as fuzzy. This situation means that the decision maker admits the violation of these constraints according to a value of tolerance. This relaxation can generate a set of solutions with very low costs but its non-fulfillment grade of the capacity constraints can be high and vice versa. This fuzzy variant is generalized in this work from a multiobjective approach by incorporating an objective to minimize the violation of constraints. We present and discuss the computational experiments carried out to solve the multiobjective Truck and Trailer Routing Problem with fuzzy constraint using benchmark instances with sizes ranging from 50 to 199 customers.
Contribuciones en Soft Computing, 2014
Studies in Computational Intelligence, 2014
Regression analysis, which includes any techniques for modeling and analyzing several variables, ... more Regression analysis, which includes any techniques for modeling and analyzing several variables, is a statistical tool that focuses in finding a relationship between a dependent variable and one or more independent variables. When this relationship is found, some values of parameters are determined which help a function to best fit in a set of data observations. In regression analysis, it is also interesting to characterize the variation of the depend variable around the independent ones. A regression problem can be formulated as a mathematical programming problem, where the objective is to minimize the difference between the estimated values and the observed values. This proposal provides a fuzzy solution to the problem that involves all particular -punctual- solutions provided by other methods. To clarify the above developments, a numerical example about the price mechanism of prefabricated houses is analyzed.
Nature Inspired Cooperative Strategies for Optimization (NICSO 2011), 2011
Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), 2011
Many real world optimization problems are dynamic, meaning that their optimal solutions are time-... more Many real world optimization problems are dynamic, meaning that their optimal solutions are time-varying. In recent years, an effective approach to address these problems has been the multi-swarmPSO (mPSO). Despite this, we believe that there is still room for improvement and, in this contribution we propose two simple strategies to increase the effectiveness of mPSO. The first one faces the diversity loss in the swarm after an environment change; while the second one increases the efficiency through stopping swarms showing a bad behavior. From the experiments performed on the Moving Peaks Benchmark, we have confirmed the benefits of our strategies.