Parallel N-ary speculative computation of simulated annealing

Sohn, Andrew

doi:10.1109/71.473510

Cited by 34 publications

(22 citation statements)

References 12 publications

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“…State-of-the-art Graphical Processing Units (GPUs) on the other hand can handle a large number of parallel threads. This allows for very quick calculation of the nonlinear system dynamics used for the predictive part of the optimisation as well parallelising the MCMC algorithms (Sohn (1995).) Considering these points, using a combination of parallel processing and high speed reconfigurable logic of GPU technology, practical implementation may be possible currently or in the near future.…”

Section: Discussionmentioning

confidence: 99%

Particle predictive control

Villiers

Godsill

Singh

2011

Journal of Statistical Planning and Inference

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This work explores the use of sequential and batch Monte Carlo techniques to solve the Nonlinear Model Predictive Control (NMPC) problem with stochastic system dynamics and noisy state observations. This is done by treating the state inference and control optimisation problems jointly as a single artificial inference problem on an augmented state-control space. The methodology is demonstrated on the benchmark car-up-the-hill problem as well as an advanced F-16 aircraft terrain following problem.

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Section: Discussionmentioning

confidence: 99%

Particle predictive control

Villiers

Godsill

Singh

2011

Journal of Statistical Planning and Inference

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“…For this motive, data parallel approaches strongly depend on problem-specific coding and we do not deal with them. In speculative algorithms [14][15][16], operations of annealing are pipelined and later decisions may result in rejection of prior calculations. This approach possesses the great advantage of maintaining, in its most radical form, entirely the same decision sequence, hence the same convergence property, as a sequential version.…”

Section: Algorithmsmentioning

confidence: 99%

“…Speculative algorithms presented in [14,15] run synchronously on a 1024 nodes shared memory multiprocessor, but use message passing libraries. They introduce a small overhead due to communication and show good performance at middle-lowtemperature regions.…”

Section: Overview Of Previous Approachesmentioning

confidence: 99%

A Methodological Approach to Parallel Simulated Annealing on an SMP System

Bevilacqua¹

2002

Journal of Parallel and Distributed Computing

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“…Moreover, the concept is a case dependent one and even may not fit to a bounded range, e.g. in some articles it is even initialized in a range from 0.001 to 100 [11]. There are a few research papers that suggest a formulation to relate the initial temperature to particular characteristics of the problem.…”

Section: Introductionmentioning

confidence: 99%

Importance of the Initial Conditions and the Time Schedule in the Simulated Annealing

Shojaee¹,

Shakouri²,

Behnam³

2010

Simulated Annealing, Theory With Applications

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It is a long time that the Simulated Annealing (SA) procedure is introduced as a nonderivative based optimization for solving NP-hard problems. Improvements from the original algorithm in the recent decade mostly concentrate on combining its initial algorithm with some heuristic methods. This is while modifications are rarely happened to the initial condition selection methods from which the annealing schedules starts or the time schedule itself. There are several parameters in the process of annealing the adjustment of which affects the overall performance. This paper focuses on the initial temperature and proposes a lower temperature with low energy to speed up the process, while using an auxiliary memory to buffer the best solution. Such an annealing indeed starts from a mushy state rather than a quite liquid molten material. The mushy state characteristics depends on the problem that SA is being applied to solve. In this paper the Mushy State Simulated Annealing (MSSA) is applied to the Traveling Salesman Problem (TSP). The mushy state may be obtained by some simple methods like crossover elimination. A very fast version of a Wise Traveling Salesman, who starts from a randomly chosen city and seeks for the nearest one as the next, is also applied to initiate SA by a low-energy-low-temperature state. This fast method results in quite accurate solutions compared to other recent novel methods.

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Parallel N-ary speculative computation of simulated annealing

Cited by 34 publications

References 12 publications

Particle predictive control

Particle predictive control

A Methodological Approach to Parallel Simulated Annealing on an SMP System

Importance of the Initial Conditions and the Time Schedule in the Simulated Annealing

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