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Simulated annealing method

Xem 1-9 trên 9 kết quả Simulated annealing method
  • The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine....

    pdf0p cucdai_1 22-10-2012 44 2   Download

  • In this study, a Cloud Scalable Multi-Objective Cat Swarm Optimization-based Simulated Annealing algorithm is proposed. In the proposed method, the orthogonal Taguchi approach is applied to enhance the SA which is incorporated into the local search of the proposed CSMCSOSA algorithm for scalability performance.

    pdf33p meriday 20-04-2019 7 0   Download

  • This book presents state of the art contributes to Simulated Annealing (SA) that is a well-known probabilistic meta-heuristic. It is used to solve discrete and continuous optimization problems. The significant advantage of SA over other solution methods has made it a practical solution method for solving complex optimization problems. Book is consisted of 13 chapters, classified in single and multiple objectives applications and it provides the reader with the knowledge of SA and several applications.

    pdf0p cucdai_1 19-10-2012 32 6   Download

  • other than this, automation and optimization of production engineering was attempted in various fields. okumoto et al. (2005d) optimized the block allocation on the assembly area using simulated annealing method. wibisono et al. (2007) optimized block division planning using genetic algorithm and product model. universal shipbuilding corporation (2008) developed a high performance nc printing machine, which enables fast printing irrespective of the number of characters and lines to be printed.

    pdf64p quynho77 13-11-2012 32 5   Download

  • Physical theories allow us to make predictions: given a complete description of a physical system, we can predict the outcome of some measurements. This problem of predicting the result of measurements is called the modelization problem, the simulation problem, or the forward problem. The inverse problem consists of using the actual result of some measurements to infer the values of the parameters that characterize the system. While the forward problemhas (in deterministic physics) a unique solution, the inverse problem does not.

    pdf358p tiramisu0908 25-10-2012 93 5   Download

  • We propose a two-step inversion of three-component seismograms that ( 1) recovers the far-field source time function at each station and (2) estimates the distribution of co-seismic slip on the fault plane for small earthquakes (magnitude 3 to 4). The empirical Green’s function (EGF) method consists of finding a small earthquake located near the one we wish to study and then performing a deconvolution to remove the path, site, and instrumental effects from the main-event signal.

    pdf13p giamdocamnhac 06-04-2013 45 3   Download

  • Outline: Memory-bounded heuristic search, Hill-climbing search  Simulated annealing search. Some solutions to A* space problems (maintain completeness and optimality). Iterative Deeping version of A*, still admissible.

    pdf16p lalala06 02-12-2015 15 1   Download

  • In this study, we consider a variant of the Bilevel Uncapacitated Facility Location Problem (BLUFLP), in which the clients choose suppliers based on their own preferences. We propose and compare three metaheuristic approaches for solving this problem: Particle Swarm Optimization (PSO), Simulated Annealing (SA), and a combination of Reduced and Basic Variable Neighborhood Search Method (VNS).

    pdf18p vinguyentuongdanh 19-12-2018 3 0   Download

  • The growing interest in the application of artificial intelligence (AI) techniques to power system engineering has introduced the potentials of using this state-of-the-art technology. AI techniques, unlike strict mathematical methods, have the apparent ability to adapt to nonlinearities and discontinuities commonly found in power systems. The best-known algorithms in this class include evolution programming, genetic algorithms, simulated annealing, tabu search, and neural networks. In the last three decades many papers on these applications have been published.

    pdf0p nguyenthai_thinh 16-03-2013 44 17   Download

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