Results 241 to 250 of about 2,844,778 (299)
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A new simulated annealing algorithm
International Journal of Computer Mathematics, 1995Simulated 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.
Xin Yao
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Parallel recombinative simulated annealing: A genetic algorithm
Parallel Computing, 1995zbMATH Open Web Interface contents unavailable due to conflicting licenses.
David E Goldberg
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Parallel Simulated Annealing Algorithms
Journal of Parallel and Distributed Computing, 1996Simulated annealing (SA) has been considered a good tool for complex nonlinear optimization problems. The technique has been widely applied to a variety of problems. However, a major disadvantage of the technique is that it is extremely slow and hence not suitable for complex optimization problems such as scheduling.
D. Janaki Ram +2 more
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A parallel simulated annealing algorithm
Parallel Computing, 1993zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nicolas Boissin, Jean-Luc Lutton
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Aspiration Based Simulated Annealing Algorithm
Journal of Global Optimization, 1997zbMATH Open Web Interface contents unavailable due to conflicting licenses.
M. M. Ali, C. Storey
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Analysis of Static Simulated Annealing Algorithms
Journal of Optimization Theory and Applications, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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, 2011In 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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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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