Results 171 to 180 of about 1,616,775 (220)
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IEEE Internet of Things Journal, 2021
Smart mobile devices (SMDs) can meet users’ high expectations by executing computational intensive applications but they only have limited resources, including CPU, memory, battery power, and wireless medium.
J. Bi +4 more
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Smart mobile devices (SMDs) can meet users’ high expectations by executing computational intensive applications but they only have limited resources, including CPU, memory, battery power, and wireless medium.
J. Bi +4 more
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RETRACTED: Indoor 3-D localization based on simulated annealing bat algorithm
The International Journal of Electrical Engineering & Education, 2020The non-line of sight (NLOS) propagation caused by the building shielding and background interference will lead to large error when applying Ultra-Wideband (UWB) technology to indoor positioning.
Liang Zhang +3 more
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Parallel Simulated Annealing with a Greedy Algorithm for Bayesian Network Structure Learning
IEEE Transactions on Knowledge and Data Engineering, 2020We present a hybrid algorithm called parallel simulated annealing with a greedy algorithm (PSAGA) to learn Bayesian network structures. This work focuses on simulated annealing and its parallelization with memoization to accelerate the search process. At
Sangmin Lee, S. Kim
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2016
Metaheuristics exhibit desirable properties like simplicity, easy parallelizability, and ready applicability to different types of optimization problems. After a comprehensive introduction to the field, the contributed chapters in this book include explanations of the main metaheuristics techniques, including simulated annealing, tabu search ...
Ke-Lin Du, M. N. S. Swamy
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Metaheuristics exhibit desirable properties like simplicity, easy parallelizability, and ready applicability to different types of optimization problems. After a comprehensive introduction to the field, the contributed chapters in this book include explanations of the main metaheuristics techniques, including simulated annealing, tabu search ...
Ke-Lin Du, M. N. S. Swamy
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Advanced Optimization and Decision-Making Techniques in Textile Manufacturing, 2019
Anindya Ghosh, P. Mal, Abhijit Majumdar
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Anindya Ghosh, P. Mal, Abhijit Majumdar
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IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2010
We present a new class of methods for the global optimization of continuous variables based on simulated annealing (SA). The coupled SA (CSA) class is characterized by a set of parallel SA processes coupled by their acceptance probabilities. The coupling is performed by a term in the acceptance probability function, which is a function of the energies ...
Samuel, Xavier-de-Souza +3 more
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We present a new class of methods for the global optimization of continuous variables based on simulated annealing (SA). The coupled SA (CSA) class is characterized by a set of parallel SA processes coupled by their acceptance probabilities. The coupling is performed by a term in the acceptance probability function, which is a function of the energies ...
Samuel, Xavier-de-Souza +3 more
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ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2017
Nazmul Siddique, Hojjat Adeli
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Nazmul Siddique, Hojjat Adeli
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SIAM Journal on Optimization, 1995
Summary: By cooling slightly more slowly than the canonical schedule and simulating direct self-loop sequences implicitly, the computer time to execute simulated annealing given the number of accepted moves becomes proportional to that number in expectation and, in a certain sense, almost surely.
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Summary: By cooling slightly more slowly than the canonical schedule and simulating direct self-loop sequences implicitly, the computer time to execute simulated annealing given the number of accepted moves becomes proportional to that number in expectation and, in a certain sense, almost surely.
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American Journal of Mathematical and Management Sciences, 1988
SYNOPTIC ABSTRACTThe principal shortcoming of simulated annealing (SA) is that it takes too much computer time. We present a few “swindling” ideas for speeding up SA by simulating its action on a problem. The increase in speed is attained at the cost of decreasing generality — the methods all require the use of problem-specific information.
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SYNOPTIC ABSTRACTThe principal shortcoming of simulated annealing (SA) is that it takes too much computer time. We present a few “swindling” ideas for speeding up SA by simulating its action on a problem. The increase in speed is attained at the cost of decreasing generality — the methods all require the use of problem-specific information.
openaire +1 more source