An analytically derived cooling schedule for simulated annealing

Shen, Yunli; Kiatsupaibul, Seksan; Zabinsky, Zelda B.; Smith, Robert L.

doi:10.1007/s10898-006-9068-2

Cited by 30 publications

(25 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hit-and-run has been successfully applied to optimization, initially continuous problems, and expanded to mixed continuous/integer problems (Bertsimas and Vempala 2004;Kalai and Vempala 2006;Mete et al 2011;Romeijn and Smith 1994;Shen et al 2007;Zabinsky 2003;Zabinsky et al 1993).…”

Section: Hit-and-run For Global Optimizationmentioning

confidence: 99%

“…For a general cooling schedule, Romeijn and Smith (1994) showed that if Hide-and-Seek ran long enough at each temperature value to converge to its stationary Boltzmann distribution, then the number of these temperature values would be linear in dimension. This led to an analytically derived adaptive cooling schedule, which was later extended to apply to both continuous and discrete global optimization problems (Shen et al 2007). The analysis was motivated by the result that a sequence of such Boltzmann distributions achieves a linear complexity on the average number of function evaluations.…”

Section: H Hmentioning

confidence: 99%

“…Diverse applications of hit-and-run include: identifying non-redundant constraints in linear programs (Berbee et al 1987); evaluation of multidimensional integrals (Chen and Schmeiser 1996); volume estimation of convex sets (Kannan et al 1997); statistical model validation; construction of a confidence interval for Bayesian inference; discrete-event simulation (Rubinstein and Kroese 2008), and global optimization (Bertsimas and Vempala 2004;Kalai and Vempala 2006;Mete et al 2011;Romeijn and Smith 1994;Shen et al 2007;Zabinsky 2003;Zabinsky et al 1992Zabinsky et al , 1993.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Half Space

2013

Encyclopedia of Operations Research and Management Science

View full text Add to dashboard Cite

Section: Hit-and-run For Global Optimizationmentioning

confidence: 99%

Section: H Hmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Half Space

2013

Encyclopedia of Operations Research and Management Science

View full text Add to dashboard Cite

“…There are several types of optimizations which allow to find global extreme of multicriteria function (Integrated Objective Function-IOF), which is based on stochastic algorithms that use probability theory to define iterative method. Group of these algorithms includes, among others: genetic algorithm, clustering algorithm, evolution algorithm, and simulating annealing, accelerated random search, ant colony optimization artificial bee colony (ABC) algorithm [6][7][8][9][10]. In stochastic optimization algorithm the objective is to find points in which the given integrated objective function (IOF) reaches its global extreme.…”

Section: Introductionmentioning

confidence: 99%

Optimization of energy cost in water supply system

Zimoch

Bartkiewicz

2017

E3S Web Conf.

View full text Add to dashboard Cite

Abstract. The decreasing amount of fossil fuels and deteriorating air quality forces the governments to introduce a rational energy management in all sectors of economy. At the beginning of the twenty-first century many water supply systems (WSSs) were oversized because of the reduction of water consumption, especially in industry. This resulted in high energy consumption in the pumping stations. Improving pumps operation will decrease energy consumption and also the water prices. The purpose of this paper is to present a method of energy optimization in WSSs. This paper presents an analysis of energy consumption in a selected water supply system. In this study pumps located in water treatment plants and pumping station cooperating with the tanks are analyzed. The study used hydraulic model of the WSS created in MOSKAN-W, which defines pumps' parameters such as flow, head, and efficiency. Using optimization options of calculation software several scenarios of energy costs were prepared.

show abstract

“…The ideal version of the Simulated Annealing algorithm requires a neighborhood generation mechanism that samples according to a sequence of Boltzmann distributions [23,26]. For specific combinatorial problems, such as the Traveling Salesman Problem, specialized neighborhood generators have been developed to be used in random sampling in optimization algorithms [3,10,27].…”

Section: Introductionmentioning

confidence: 99%

Pattern discrete and mixed Hit-and-Run for global optimization

et al. 2010

Self Cite

View full text Add to dashboard Cite

We develop new Markov chain Monte Carlo samplers for neighborhood generation in global optimization algorithms based on Hit-and-Run. The success of Hit-and-Run as a sampler on continuous domains motivated Discrete Hit-and-Run with random biwalk for discrete domains. However, the potential in efficiencies in the implementation, which requires a randomization at each move to create the biwalk, lead us to a different approach that uses fixed patterns in generating the biwalks. We define Sphere and Box Biwalks that are pattern-based and easily implemented for discrete and mixed continuous/discrete domains. The pattern-based Hit-and-Run Markov chains preserve the convergence properties of Hitand-Run to a target distribution. They also converge to continuous Hit-and-Run as the mesh of the discretized variables becomes finer, approaching a continuum. Moreover, we provide bounds on the finite time performance for the discrete cases of Sphere and Box Biwalks. We embed our samplers in an Improving Hit-and-Run global optimization algorithm and test their performance on a number of global optimization test problems.

show abstract

An analytically derived cooling schedule for simulated annealing

Cited by 30 publications

References 23 publications

Half Space

Half Space

Optimization of energy cost in water supply system

Pattern discrete and mixed Hit-and-Run for global optimization

Contact Info

Product

Resources

About