Search results for "Adaptive simulated annealing"

showing 8 items of 8 documents

Simulated Annealing Technique for Fast Learning of SOM Networks

2011

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input datasets. In this paper, we present an application of the simulated annealing procedure to the SOM learning algorithm with the aim to obtain a fast learning and better performances in terms of quantization error. The proposed learning algorithm is called Fast Learning Self-Organized Map, and it does not affect the easiness of the basic learning algorithm of the standard SOM. The proposed learning algorithm also improves the quality of resulting maps by providing better clustering quality and topology preservation of input multi-dimensi…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniComputer Science::Machine LearningArtificial IntelligenceSOM Simulated annealing Clustering Fast learningArtificial neural networkWake-sleep algorithmbusiness.industryComputer scienceTopology (electrical circuits)computer.software_genreAdaptive simulated annealingGeneralization errorData visualizationComputingMethodologies_PATTERNRECOGNITIONArtificial IntelligenceSimulated annealingUnsupervised learningData miningbusinessCluster analysisSelf Organizing map simulated annealingcomputerSoftware
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A new innovative cooling law for simulated annealing algorithms

2015

The present paper proposes an original and innovative cooling law in the field of Simulated Annealing (SA) algorithms. Particularly, such a law is based on the evolution of different initial seeds on which the algorithm works in parallel. The efficiency control of the new proposal, executed on problems of different kind, shows that the convergence quickness by using such a new cooling law is considerably greater than that obtained by traditional laws. Furthermore, it is shown that the effectiveness of the SA algorithm arising from the proposed cooling law is independent of the problem type. This last feature reduces the number of parameters to be initially fixed, so simplifying the prelimin…

EngineeringMathematical optimizationCooling lawMultidisciplinarybusiness.industryJob shopProject crashingProcess (computing)Job-shopAdaptive simulated annealingField (computer science)Simulated annealingLawAlgorithmic efficiencySimulated annealingConvergence (routing)businessAlgorithmA-law algorithm
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Investigation of acceptance simulated annealing — A simplified approach to adaptive cooling schedules

2010

Abstract Simulated annealing is the classic physical optimization algorithm, which has been applied to a large variety of problems for many years. Over time, several adaptive mechanisms for decreasing the temperature and thus controlling the acceptance of deteriorations have been developed, based on the measurement of the mean value and the variance of the energy. Here we propose a new simplified approach in which we consider the probability of accepting deteriorations as the main control parameter and derive the temperature by averaging over the last few deteriorations stored in a memory. We present results for the traveling salesman problem and demonstrate, how the amount of data retained…

Statistics and ProbabilityMathematical optimizationScheduleComputer scienceSimulated annealingVariance (accounting)Condensed Matter PhysicsAdaptive simulated annealingTravelling salesman problemEnergy (signal processing)Physica A: Statistical Mechanics and its Applications
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Domain-Knowledge Optimized Simulated Annealing for Network-on-Chip Application Mapping

2013

Network-on-Chip architectures are scalable on-chip interconnection networks. They replace the inefficient shared buses and are suitable for multicore and manycore systems. This paper presents an Optimized Simulated Annealing (OSA) algorithm for the Network-on-Chip application mapping problem. With OSA, the cores are implicitly and dynamically clustered using knowledge about communication demands. We show that OSA is a more feasible Simulated Annealing approach to NoC application mapping by comparing it with a general Simulated Annealing algorithm and a Branch and Bound algorithm, too. Using real applications we show that OSA is significantly faster than a general Simulated Annealing, withou…

Computer Science::Hardware ArchitectureInterconnectionMulti-core processorNetwork on a chipBranch and boundComputer scienceScalabilitySimulated annealingComputer Science::Networking and Internet ArchitectureParallel computingAdaptive simulated annealingCluster analysis
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Simulated Annealing in Bayesian Decision Theory

1992

Since the seminal paper by Kirkpatrick, Gelatt and Vechhi (1983), a number of papers in the scientific literature refer to simulated annealing as a powerful random optimization method which promises to deliver, within reasonable computing times, optimal or nearly optimal solutions to complex decision problems hitherto forbidding. The algorithm, which uses the physical process of annealing as a metaphor, is special in that, at each iteration, one may move with positive probability to solutions with higher values of the function to minimize, rather than directly jumping to the point with the smallest value within the neighborhood, thus drastically reducing the chances of getting trapped in lo…

Maxima and minimaMathematical optimizationBayes estimatorSimulated annealingBayesian probabilityRandom optimizationContext (language use)Decision problemAdaptive simulated annealingMathematics
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Improved SOM Learning using Simulated Annealing

2007

Self-Organizing Map (SOM) algorithm has been extensively used for analysis and classification problems. For this kind of problems, datasets become more and more large and it is necessary to speed up the SOM learning. In this paper we present an application of the Simulated Annealing (SA) procedure to the SOM learning algorithm. The goal of the algorithm is to obtain fast learning and better performance in terms of matching of input data and regularity of the obtained map. An advantage of the proposed technique is that it preserves the simplicity of the basic algorithm. Several tests, carried out on different large datasets, demonstrate the effectiveness of the proposed algorithm in comparis…

SpeedupMatching (graph theory)Wake-sleep algorithmComputer sciencebusiness.industryPattern recognitioncomputer.software_genreAdaptive simulated annealingGeneralization errorComputingMethodologies_PATTERNRECOGNITIONSimulated annealingSOM simulated Annealing TrainingData miningArtificial intelligencebusinesscomputer
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A comparison of simplex and simulated annealing for optimization of a new rear underrun protective device

2012

In this paper, two optimization approaches to improve the product design process have been analysed. Through the analysis of a case study, concerning the designing of a new High Energy Absorption Rear Underrun Protective Device (HEARUPD), two different optimization approaches (simplex and simulated annealing) have been compared. In the implemented optimization processes, the crash between an economy car and the rear part of a truck has been simulated by dynamic numerical (FEM) analyses. Moreover, authors have proposed the use of a suitable linear function of four variables with the purpose of reducing the multi-objective optimization processes to mono-objective ones. That has been made to s…

State variableEngineeringMathematical optimizationSimplexOptimization problembusiness.industryGeneral EngineeringOptimization Simulated annealing Simplex Numerical crash analysisAdaptive simulated annealingLinear functionFinite element methodComputer Science ApplicationsSimplex algorithmModeling and SimulationSimulated annealingSettore ING-IND/15 - Disegno E Metodi Dell'Ingegneria IndustrialebusinessSoftwareEngineering with Computers
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Implementing some Evolutionary Computing Methods for Determining the Optimal Parameters in the Turning Process

2015

In this paper, we comparatively present two heuristics search methods – Simulated Annealing and Weighted Sum Genetic Algorithm, in order to find optimal cutting parameters in turning operation. We consider five different constraints aiming to achieve minimum total cost of machining. We developed a customizable software application in Microsoft Visual Studio with C# source code, flexible and extensible that implements the optimization methods. The experiments are based on real data gathered from S.C. “Compa” S.A Sibiu, a company that manufactures automotive components and targets improving of product quality and reducing cost and production time. The obtained results show that, although the …

Mathematical optimizationEngineeringSource codebusiness.industrymedia_common.quotation_subjectGeneral MedicineMachine learningcomputer.software_genreAdaptive simulated annealingEvolutionary computationMicrosoft Visual StudioSoftwareSimulated annealingGenetic algorithmArtificial intelligenceHeuristicsbusinesscomputermedia_commonApplied Mechanics and Materials
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