Search results for "simulated annealing"
showing 10 items of 63 documents
Multilayer neural networks: an experimental evaluation of on-line training methods
2004
Artificial neural networks (ANN) are inspired by the structure of biological neural networks and their ability to integrate knowledge and learning. In ANN training, the objective is to minimize the error over the training set. The most popular method for training these networks is back propagation, a gradient descent technique. Other non-linear optimization methods such as conjugate directions set or conjugate gradient have also been used for this purpose. Recently, metaheuristics such as simulated annealing, genetic algorithms or tabu search have been also adapted to this context.There are situations in which the necessary training data are being generated in real time and, an extensive tr…
Flexible Spare Core Placement in Torus Topology based NoCs and its validation on an FPGA
2021
In the nano-scale era, Network-on-Chip (NoC) interconnection paradigm has gained importance to abide by the communication challenges in Chip Multi-Processors (CMPs). With increased integration density on CMPs, NoC components namely cores, routers, and links are susceptible to failures. Therefore, to improve system reliability, there is a need for efficient fault-tolerant techniques that mitigate permanent faults in NoC based CMPs. There exists several fault-tolerant techniques that address the permanent faults in application cores while placing the spare cores onto NoC topologies. However, these techniques are limited to Mesh topology based NoCs. There are few approaches that have realized …
Influence of rounding errors on the quality of heuristic optimization algorithms
2011
Abstract Search space smoothing and related heuristic optimization algorithms provide an alternative approach to simulated annealing and its variants: while simulated annealing traverses barriers in the energy landscape at finite temperatures, search space smoothing intends to remove these barriers, so that a greedy algorithm is sufficient to find the global minimum. Several formulas for smoothing the energy landscape have already been applied, one of them making use of the finite numerical precision on a computer. In this paper, we thoroughly investigate the effect of finite numerical accuracy on the quality of results achieved with heuristic optimization algorithms. We present computation…
Randomized heuristics for the Capacitated Clustering Problem
2017
In this paper, we investigate the adaptation of the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem (CCP). In particular, we focus on the effect of the balance between randomization and greediness on the performance of these multi-start heuristic search methods when solving this NP-hard problem. The former is a memory-less approach that constructs independent solutions, while the latter is a memory-based method that constructs linked solutions, obtained by partially rebuilding previous ones. Both are based on the combination of greediness and randomization in the constructive process, and coupled with a subsequent l…
A model for designing callable bonds and its solution using tabu search
1997
Abstract We formulate the problem of designing callable bonds as a non-linear, global, optimization problem. The data of the model are obtained from simulations of holding-period returns of a given bond design, which are used to compute a certainty equivalent return, viz., some target assets. The design specifications of the callable bond are then adjusted so that the certainty equivalent return is maximized. The resulting problem is multi-modal, and a tabu search procedure, implemented on a distributed network of workstations, is used to optimize the bond design. The model is compared with the classical portfolio immunization model, and the tabu search solution technique is compared with s…
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…
Ant Colony Search algorithm for optimal strategical planning of electrical distribution systems expansion
2005
Strategical planning is one of many research fields in the design of electrical distribution systems. The problem of strategical planning is a multiobjective combinatorial problem and the search space may often be quite large concerning to the options. The aim is to identify a strategy of expansion of a given distribution system in a given timeframe. For this problem, the search space is created beforehand by running a multiobjective optimisation algorithm for the optimal design of distribution networks for different load levels related to different years. The sets of Pareto-optimal solutions obtained for each load level at each year are equivalent in terms of the considered objectives, the…
Biased Modern Heuristics for the OCST Problem
2011
Biasing modern heuristics is an appropriate possibility in designing problem-specific and high-quality modern heuristics. If we have knowledge about a problem we can bias the design elements of modern heuristics, namely the representation and search operator, fitness function, the initial solution, or even the search strategy. This chapter presents a case study on how the performance of modern heuristics can be increased by biasing the design elements towards high-quality solutions. Results show that problem-specific and biased modern heuristics outperform standard variants and even for large problem instances high-quality solutions can be found.
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…
Fast Training of Self Organizing Maps for the Visual Exploration of Molecular Compounds
2007
Visual exploration of scientific data in life science\ud area is a growing research field due to the large amount of\ud available data. The Kohonen’s Self Organizing Map (SOM) is\ud a widely used tool for visualization of multidimensional data.\ud In this paper we present a fast learning algorithm for SOMs\ud that uses a simulated annealing method to adapt the learning\ud parameters. The algorithm has been adopted in a data analysis\ud framework for the generation of similarity maps. Such maps\ud provide an effective tool for the visual exploration of large and\ud multi-dimensional input spaces. The approach has been applied\ud to data generated during the High Throughput Screening\ud of mo…