Simulated annealing on a multiprocessor

Chamberlain, Roger D.; Edelman, Mark N.; Franklin, Mark A.; Witte, E.E.

doi:10.1109/iccd.1988.25758

Cited by 16 publications

(4 citation statements)

References 7 publications

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“…Parallelisation of simulated annealing type algorithms were studied by several authors. A precursor work is [34], where the simulated annealing algorithm is mapped onto a dynamically structured tree of processors; the algorithm presented achieves speedups between log 2 N and (N + log 2 N)/2, N being the number of processors. Another important work is a volume edited by Robert Azencott, from which [35] is a paradigmatic chapter.…”

Section: Introductionmentioning

confidence: 99%

On a Parallelised Diffusion Induced Stochastic Algorithm with Pure Random Search Steps for Global Optimisation

et al. 2021

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We propose a stochastic algorithm for global optimisation of a regular function, possibly unbounded, defined on a bounded set with regular boundary; a function that attains its extremum in the boundary of its domain of definition. The algorithm is determined by a diffusion process that is associated with the function by means of a strictly elliptic operator that ensures an adequate maximum principle. In order to preclude the algorithm to be trapped in a local extremum, we add a pure random search step to the algorithm. We show that an adequate procedure of parallelisation of the algorithm can increase the rate of convergence, thus superseding the main drawback of the addition of the pure random search step.

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Section: Introductionmentioning

confidence: 99%

On a Parallelised Diffusion Induced Stochastic Algorithm with Pure Random Search Steps for Global Optimisation

et al. 2021

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“…In this paper we are interested in the problem of optimal ship routing taking into account only the wave height and direction by using the simulated annealing algorithm [5,6]. The simulated annealing method is an extension of a Monte Carlo method developed by Metropolis et al [7], to determine the equilibrium states of a collection of atoms at any given temperature T. Since the method was first proposed in [5,6], much research has been conducted on its use and properties [8,9,10,11,12,13,14,15]. The method itself is a technique which has attracted significant attention as suitable for optimization problems of large scale.…”

Section: Introductionmentioning

confidence: 99%

Simulated annealing for optimal ship routing

Kosmas

Vlachos

2012

Computers & Operations Research

102

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a b s t r a c tIn this article we present a simulated annealing based algorithm for the determination of optimal ship routes through the minimization of a cost function defined as a weighted sum of the time of voyage and the voyage comfort (safety is taken into account too). This cost function is dependent on the wind speed and its direction as well as on the wave height and its direction. The constructed algorithm at the beginning discretizes an initial route and then optimizes it by considering small deviations, which are accepted or rejected by utilizing the simulated annealing technique. Using calculus of variations, we prove a key theorem which tremendously accelerates the convergence of the proposed algorithm. For an illustration of the advantages of the constructed method, both computational and real experiments have been carried out which are presented and discussed.

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“…We compare the performance of nine typical scheduling algorithms: min-min [4], chaining [5], A * [6], genetic algorithms [1], simulated annealing [7], tabu search [8], as well as three popular list scheduling heuristics [9]: Highest Level First Known Execution Times (HLFET) [10], Insertion Scheduling Heuristic (ISH) [11], and Duplication Scheduling Heuristic (DSH) [12]. Since some of the heuristics were not originally designed to solve the problem in the presence of communication delays, we have adapted the algorithms to take into consideration the communication delays in these cases.…”

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confidence: 99%

A performance study of multiprocessor task scheduling algorithms

2007

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Multiprocessor task scheduling is an important and computationally difficult problem. A large number of algorithms were proposed which represent various tradeoffs between the quality of the solution and the computational complexity and scalability of the algorithm. Previous comparison studies have frequently operated with simplifying assumptions, such as independent tasks, artificially generated problems or the assumption of zero communication delay. In this paper, we propose a comparison study with realistic assumptions. Our target problems are two well known problems of linear algebra: LU decomposition and Gauss-Jordan elimination. Both algorithms are naturally parallelizable but have heavy data dependencies. The communication delay will be explicitly considered in the comparisons. In our study, we consider nine scheduling algorithms which are frequently used to the best of our knowledge: min-min, chaining, A * , genetic algorithms, simulated annealing, tabu search, HLFET, ISH, and DSH with task duplication. Based on experimental results, we present a detailed analysis of the scalability, advantages and disadvantages of each algorithm.

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Simulated annealing on a multiprocessor

Cited by 16 publications

References 7 publications

On a Parallelised Diffusion Induced Stochastic Algorithm with Pure Random Search Steps for Global Optimisation

On a Parallelised Diffusion Induced Stochastic Algorithm with Pure Random Search Steps for Global Optimisation

Simulated annealing for optimal ship routing

A performance study of multiprocessor task scheduling algorithms

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