5,298 research outputs found
Genetic embedded matching approach to ground states in continuous-spin systems
Due to an extremely rugged structure of the free energy landscape, the
determination of spin-glass ground states is among the hardest known
optimization problems, found to be NP-hard in the most general case. Owing to
the specific structure of local (free) energy minima, general-purpose
optimization strategies perform relatively poorly on these problems, and a
number of specially tailored optimization techniques have been developed in
particular for the Ising spin glass and similar discrete systems. Here, an
efficient optimization heuristic for the much less discussed case of continuous
spins is introduced, based on the combination of an embedding of Ising spins
into the continuous rotators and an appropriate variant of a genetic algorithm.
Statistical techniques for insuring high reliability in finding (numerically)
exact ground states are discussed, and the method is benchmarked against the
simulated annealing approach.Comment: 17 pages, 12 figures, 1 tabl
Optimal Instrumentation: Adjustment and Hybridization of a Simulated Annealing Based Technique
This work presents an analysis on the behavior of a particular metaheuristic technique inspired by Simulated Annealing, SA, for the resolution of a problem of sensor network design in process plants, SNDP. This design problem is formulated as a combinatorial optimization problem and includes the selection and determination of the number of process variables that must be measured to achieve the specific state of knowledge about the plant.
Different parametric configurations for the proposed heuristic are analyzed and discussed, as well as their performance in plants of increasing size and complexity.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic
Ajustes de un algoritmo hÃbrido basado en SA aplicado al diseño óptimo de redes de sensores
Sensor network design problem (SNDP) in process plants includes the determination of which process variables should be measured to achieve a required degree of knowledge about the plant. We propose to solve the SNDP problem in plants of increasing size and complexity using a hybrid algorithm based on Simulated Annealing (HSA) as main metaheuristic and Tabu Search embedded with Strategic Oscillation (SOTS) as a subordinate metaheuristic. We studied the tuning of control parameters in order to improve the HSA performance. Experimental results indicate that a high-quality solution in reasonable computational times can be found by HSA effectively. Moreover, HSA shows good features solving SNDP compared with proposals from the literature.Facultad de Informátic
Learning the structure of Bayesian Networks: A quantitative assessment of the effect of different algorithmic schemes
One of the most challenging tasks when adopting Bayesian Networks (BNs) is
the one of learning their structure from data. This task is complicated by the
huge search space of possible solutions, and by the fact that the problem is
NP-hard. Hence, full enumeration of all the possible solutions is not always
feasible and approximations are often required. However, to the best of our
knowledge, a quantitative analysis of the performance and characteristics of
the different heuristics to solve this problem has never been done before.
For this reason, in this work, we provide a detailed comparison of many
different state-of-the-arts methods for structural learning on simulated data
considering both BNs with discrete and continuous variables, and with different
rates of noise in the data. In particular, we investigate the performance of
different widespread scores and algorithmic approaches proposed for the
inference and the statistical pitfalls within them
Ajustes de un algoritmo hÃbrido basado en SA aplicado al diseño óptimo de redes de sensores
Sensor network design problem (SNDP) in process plants includes the determination of which process variables should be measured to achieve a required degree of knowledge about the plant. We propose to solve the SNDP problem in plants of increasing size and complexity using a hybrid algorithm based on Simulated Annealing (HSA) as main metaheuristic and Tabu Search embedded with Strategic Oscillation (SOTS) as a subordinate metaheuristic. We studied the tuning of control parameters in order to improve the HSA performance. Experimental results indicate that a high-quality solution in reasonable computational times can be found by HSA effectively. Moreover, HSA shows good features solving SNDP compared with proposals from the literature.Facultad de Informátic
Ajustes de un algoritmo hÃbrido basado en SA aplicado al diseño óptimo de redes de sensores
Sensor network design problem (SNDP) in process plants includes the determination of which process variables should be measured to achieve a required degree of knowledge about the plant. We propose to solve the SNDP problem in plants of increasing size and complexity using a hybrid algorithm based on Simulated Annealing (HSA) as main metaheuristic and Tabu Search embedded with Strategic Oscillation (SOTS) as a subordinate metaheuristic. We studied the tuning of control parameters in order to improve the HSA performance. Experimental results indicate that a high-quality solution in reasonable computational times can be found by HSA effectively. Moreover, HSA shows good features solving SNDP compared with proposals from the literature.Facultad de Informátic
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A comparison of general-purpose optimization algorithms forfinding optimal approximate experimental designs
Several common general purpose optimization algorithms are compared for findingA- and D-optimal designs for different types of statistical models of varying complexity,including high dimensional models with five and more factors. The algorithms of interestinclude exact methods, such as the interior point method, the Nelder–Mead method, theactive set method, the sequential quadratic programming, and metaheuristic algorithms,such as particle swarm optimization, simulated annealing and genetic algorithms.Several simulations are performed, which provide general recommendations on theutility and performance of each method, including hybridized versions of metaheuristicalgorithms for finding optimal experimental designs. A key result is that general-purposeoptimization algorithms, both exact methods and metaheuristic algorithms, perform wellfor finding optimal approximate experimental designs
Tuning a hybrid SA based algorithm applied to Optimal Sensor Network Design
El problema de diseño de una red de sensores en plantas de proceso (Sensor Network Design Problem, SNDP) consiste en determinar las variables de proceso que deben ser medidas, a fin de alcanzar el grado de conocimiento requerido de dicha planta. Proponemos resolver el problema SNDP en plantas de tamaño y complejidad creciente utilizando un algoritmo hÃbrido basado en Recocido Simulado (Hybrid Simulated Annealing, HSA) como metaheurÃstica principal y Búsqueda Tabú con Oscilación Estratégica como metaheurÃstica subordinada. Investigamos los ajustes de los parámetros de control para obtener el mejor desempeño del HSA. Los resultados experimentales indican que el HSA puede efectivamente encontrar una solución de buena calidad en tiempos de computo razonable. Mas a ´ un, HSA muestra buenas ´ caracterÃsticas en la solución de SNDP en comparación con algoritmos propuestos en la literatura.Sensor network design problem (SNDP) in process plants includes the determination of which process variables should be measured to achieve a required degree of knowledge about the plant. We propose to solve the SNDP problem in plants of increasing size and complexity using a hybrid algorithm based on Simulated Annealing (HSA) as main metaheuristic and Tabu Search embedded with Strategic Oscillation (SOTS) as a subordinate metaheuristic. We studied the tuning of control parameters in order to improve the HSA performance. Experimental results indicate that a high-quality solution in reasonable computational times can be found by HSA effectively. Moreover, HSA shows good features solving SNDP compared with proposals from the literature.Fil: Hernandez, Jose Luis. Universidad Nacional de RÃo Cuarto. Facultad de IngenierÃa; ArgentinaFil: Salto, Carolina. Universidad Nacional de la Pampa. Facultad de IngenierÃa. Departamento de Informatica; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - Patagonia Confluencia; ArgentinaFil: Minetti, Gabriela Fabiana. Universidad Nacional de la Pampa. Facultad de IngenierÃa. Departamento de Informatica; ArgentinaFil: Carnero, Mercedes del Carmen. Universidad Nacional de RÃo Cuarto. Facultad de IngenierÃa; ArgentinaFil: Bermudez, Carlos Alberto. Universidad Nacional de la Pampa. Facultad de IngenierÃa. Departamento de Informatica; ArgentinaFil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca. Planta Piloto de IngenierÃa QuÃmica. Universidad Nacional del Sur. Planta Piloto de IngenierÃa QuÃmica; Argentin
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