Simulated annealing method

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  • 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....

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  • 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.

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  • 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.

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  • 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.

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  • 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.

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  • 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.

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  • 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.

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