Results 241 to 250 of about 122,614 (295)

Aspiration Based Simulated Annealing Algorithm

Journal of Global Optimization, 1997
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M. M. Ali, C. Storey
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NEW SIMULATED ANNEALING ALGORITHMS FOR CONSTRAINED OPTIMIZATION [PDF]

Asia-Pacific Journal of Operational Research, 2010
We propose a Population based dual-sequence Non-Penalty Annealing algorithm (PNPA) for solving the general nonlinear constrained optimization problem. The PNPA maintains a population of solutions that are intermixed by crossover to supply a new starting solution for simulated annealing throughout the search. Every time the search gets stuck at a local
Linet Özdamar, Chandra Sekhar Pedamallu
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A simulated annealing algorithm for demand response

2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, 2011
For many consumers there are loads which need to be on for a subinterval between two time instants, but it is immaterial during which subinterval it is run. In many price based Demand Response (DR) programs such as Time of Use (TOU), Critical Peak Pricing (CPP), Extreme Day Pricing customers are informed about the prices on a day ahead bases.
T. P. Imthias Ahamed, S. Danish Maqbool, Essam A. Al-Ammar, N. H. Malik   +3 more
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Note on the Convergence of Simulated Annealing Algorithms

SIAM Journal on Control and Optimization, 1991
Generalizing the results of the first author and \textit{R. Schrader} [Inf. Process. Lett. 27, 189-194 (1988; Zbl 0638.65054)] a short inductive proof is given that shows that the stationary distributions of a simulated annealing algorithm converge to a distribution where nonoptimal elements are generated with probability zero, provided that the ``weak
Faigle, U., Kern, W.
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Analysis of Static Simulated Annealing Algorithms

Journal of Optimization Theory and Applications, 2002
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Orosz, J. E., Jacobson, S. H.
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Deployment algorithm using simulated annealing

2011 16th International Conference on Methods & Models in Automation & Robotics, 2011
In this paper, a novel information design algorithm is proposed. It is based on the simulated annealing, which is regarded as one of the important meta-heuristic optimization methods. The idea behind our algorithm originates from the search for optimal solution, where selection avoids local minima.
Slawomir Nikiel, Pawel Dabrowski
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Simulated annealing algorithm with adaptive neighborhood

Applied Soft Computing Journal, 2011
As we know, simulated annealing algorithm with large neighborhoods has greater probability of arriving at a global optimum than a small one has, if the other conditions, i.e., the initial configuration, initial temperature and temperature decreasing rate, are the same.
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A new simulated annealing algorithm

International Journal of Computer Mathematics, 1995
Simulated Annealing (SA) is a powerful stochastic search algorithm applicable to a wide range of problems for which little prior knowledge is available. The annealing schedule, i.e., the temperature decreasing rate used in SA is an important factor which affects SA's rate of convergence.
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A new multiobjective simulated annealing algorithm

Journal of Global Optimization, 2006
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Ozan Tekinalp, Gizem Karsli
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Parallel recombinative simulated annealing: A genetic algorithm

Parallel Computing, 1995
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David E Goldberg
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