Parallel recombinative simulated annealing: a genetic algorithm
Parallel Computing
Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Synchronous and Asynchronous Parallel Simulated Annealing with Multiple Markov Chains
IEEE Transactions on Parallel and Distributed Systems
Enhanced simulated annealing for globally minimizing functions of many-continuous variables
ACM Transactions on Mathematical Software (TOMS)
Parallel Genetic Simulated Annealing: A Massively Parallel SIMD Algorithm
IEEE Transactions on Parallel and Distributed Systems
Experiments with new stochastic global optimization search techniques
Computers and Operations Research
On Simulated Annealing and Nested Annealing
Journal of Global Optimization
Parallel Simulated Annealing Algorithms in Global Optimization
Journal of Global Optimization
A methodological approach to parallel simulated annealing on an SMP System
Journal of Parallel and Distributed Computing
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
A robust stochastic genetic algorithm (StGA) for global numerical optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Sample-sort simulated annealing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
VLSI module placement based on rectangle-packing by the sequence-pair
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Automatic seizure detection incorporating structural information
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Support vector methods for survival analysis: a comparison between ranking and regression approaches
Artificial Intelligence in Medicine
White box radial basis function classifiers with component selection for clinical prediction models
Artificial Intelligence in Medicine
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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 of the current states of all SA processes. A particular CSA instance method is distinguished by the form of its coupling term and acceptance probability. In this paper, we present three CSA instance methods and compare them with the uncoupled case, i.e., multistart SA. The primary objective of the coupling in CSA is to create cooperative behavior via information exchange. This aim helps in the decision of whether uphill moves will be accepted. In addition, coupling can provide information that can be used online to steer the overall optimization process toward the global optimum. We present an example where we use the acceptance temperature to control the variance of the acceptance probabilities with a simple control scheme. This approach leads to much better optimization efficiency, because it reduces the sensitivity of the algorithm to initialization parameters while guiding the optimization process to quasioptimal runs.We present the results of extensive experiments and show that the addition of the coupling and the variance control leads to considerable improvements with respect to the uncoupled case and a more recently proposed distributed version of SA.