Simulated annealing with constant thermodynamic speed

Salamon, Peter; Nulton, James; Harland, John R.; Pedersen, Jacob; Ruppeiner, George; Liao, Luby

doi:10.1016/0010-4655(88)90003-3

Cited by 73 publications

(38 citation statements)

References 11 publications

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“…Teller, & E. Teller, 1953) or the simulated annealing approach (Andresen et al, 1988;Kirkpatrick et al, 1983;Salamon, Nulton, Robinson, Pedersen, Ruppeiner, & Liao, 1988;Sibani et al, 1990). The analogy between equilibrium statistical mechanics and the Metropolis algorithm was first discussed in Kirkpatrick et al (1983). A dynamics that finds the minimum among a discrete set of scalar values, for example, energy values U,, with i = 1,. .…”

Section: Boltzmann Strategymentioning

confidence: 97%

Optimization of Road Networks Using Evolutionary Strategies

Schweitzer

Ébeling

Rosé

et al. 1997

Evolutionary Computation

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A road network usually has to fulfill two requirements: (i) it should as far as possible provide direct connections between nodes to avoid large detours; and (ii) the costs for road construction and maintenance, which are assumed proportional to the total length of the roads, should be low. The optimal solution is a compromise between these contradictory demands, which in our model can be weighted by a parameter. The road optimization problem belongs to the class of frustrated optimization problems. In this paper, a special class of evolutionary strategies, such as the Boltzmann and Darwin and mixed strategies, are applied to find differently optimized solutions (graphs of varying density) for the road network, depending on the degree of frustration. We show that the optimization process occurs on two different time scales. In the asymptotic limit, a fixed relation between the mean connection distance (detour) and the total length (costs) of the network exists that defines a range of possible compromises. Furthermore, we investigate the density of states, which describes the number of solutions with a certain fitness value in the stationary regime. We find that the network problem belongs to a class of optimization problems in which more effort in optimization certainly yields better solutions. An analytical approximation for the relation between effort and improvement is derived.

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Section: Boltzmann Strategymentioning

confidence: 97%

Optimization of Road Networks Using Evolutionary Strategies

Schweitzer

Ébeling

Rosé

et al. 1997

Evolutionary Computation

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“…It is possible to overcome this issue through the use of 'adaptive' annealing schedules such as those proposed in [17][18][19].…”

Section: Motivation For the Data Annealing Algorithmmentioning

confidence: 98%

Bayesian system identification of a nonlinear dynamical system using a novel variant of Simulated Annealing

Green

2015

Mechanical Systems and Signal Processing

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a b s t r a c tThis work details the Bayesian identification of a nonlinear dynamical system using a novel MCMC algorithm: 'Data Annealing'. Data Annealing is similar to Simulated Annealing in that it allows the Markov chain to easily clear 'local traps' in the target distribution. To achieve this, training data is fed into the likelihood such that its influence over the posterior is introduced gradually -this allows the annealing procedure to be conducted with reduced computational expense. Additionally, Data Annealing uses a proposal distribution which allows it to conduct a local search accompanied by occasional long jumps, reducing the chance that it will become stuck in local traps. Here it is used to identify an experimental nonlinear system. The resulting Markov chains are used to approximate the covariance matrices of the parameters in a set of competing models before the issue of model selection is tackled using the Deviance Information Criterion.

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“…This is also a feature of an annealing cooling schedule based on constant ''thermodynamic speed,'' where the cooling rate is inversely proportional to the heat capacity. 9,18 The advantage of using Eq. ͑1͒ is that it requires no prior knowledge of the heat capacity.…”

Section: A MD Simulated Annealingmentioning

confidence: 99%

Energy minimization using the classical density distribution: Application to sodium chloride clusters

Amara

Straub

1996

Phys. Rev. B

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The adiabatic Gaussian density annealing ͑AGDA͒ optimization algorithm, based on direct integration of the classical Bloch equation is reciprocal temperature, is employed to study the structures of ͑NaCl͒ n and ͑NaCl͒ n Na ϩ clusters. The results of the energy minimizations are compared with simulated annealing, using molecular dynamics. We model these clusters with a rigid core potential used in previous studies. Overall, the AGDA algorithm was successful in isolating the lowest-energy minima. In contrast to the conclusion of a previous study, we find that molecular-dynamics-based simulated annealing can be quite effective at isolating low-energy minima. However, the effectiveness of the method is strongly dependent on the integration algorithm employed. The hexagonal and cubic growth forms are found to be most stable for small clusters and the fcc structure is most stable for large clusters in agreement with previous studies. The difficulty in locating the global energy minimum is explained in terms of the statistical properties of the energy landscape. ͓S0163-1829͑96͒02120-0͔

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Simulated annealing with constant thermodynamic speed

Cited by 73 publications

References 11 publications

Optimization of Road Networks Using Evolutionary Strategies

Optimization of Road Networks Using Evolutionary Strategies

Bayesian system identification of a nonlinear dynamical system using a novel variant of Simulated Annealing

Energy minimization using the classical density distribution: Application to sodium chloride clusters

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