Simulated Annealing: A Pedestrian Review of the Theory and Some Applications

Aarts, Ehl Emile; Laarhoven, Peter van

doi:10.1007/978-3-642-83069-3_15

Cited by 43 publications

(32 citation statements)

References 22 publications

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“…The choice of cooling schedule can be difficult as the "best" choice will be different for each application. The investigation for an adequate cooling schedule is still a work in progress and has long been a predominant theme (Aarts and Korst, 1989;Aarts and Laarhoven, 1987;Chainate et al, 2008;Collins et al, 1987;Laarhoven, 1988;Prajapati et al, 2009). Accordingly, the following parameters were specified and chosen for their relative simplicity and expedient computation:…”

Section: Definition Of Parameters In Simulated Annealingmentioning

confidence: 99%

Development of a Simulated Annealing-assisted System for Land-use/Land-cover Classification

Wiele

Khorram

Yuan

2012

ESR

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Local minima limitations in unsupervised approaches using K-means is still problematic in producing accurate land use/land cover classifications. In response, we developed algorithms of Simulated Annealing (SA) systems based on K-means. We hypothesized that SA-based systems can reduce the likelihood of converging on a local minimum. Two automated SA-based classification systems were developed and applied to a Landsat TM data: a single SA-based (S-SA) system and an integrated SA-based (I-SA) system, which reduces computational intensity. We hypothesized that the I-SA system could produce more efficient classifications than the S-SA system. Kappa statistical analysis on the resulting error matrices demonstrated that the SA-based system significantly improved the classification accuracy over that of the K-means algorithm when appropriate parameters combined as a cooling schedule were chosen. The knowledge and insights gained can facilitate the incorporation of SA random search procedures into other approaches that are similarly limited by local minimum problems to improve accuracy.

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Section: Definition Of Parameters In Simulated Annealingmentioning

confidence: 99%

Development of a Simulated Annealing-assisted System for Land-use/Land-cover Classification

Wiele

Khorram

Yuan

2012

ESR

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“…The pseudocode of the basic SA algorithm is shown as Algorithm 1. More details of this algorithm and its applications can be found in the work of Aarts and van Laarhoven (1987). Generate a random number d in the interval (0, 1);…”

Section: Simulated Annealing Tabu Search Andmentioning

confidence: 99%

“…In the case of the simulated annealing algorithm, the initial temperature T 0 and the low temperature T L were set following the suggestions of Aarts and van Laarhoven (1987). The control temperature was calculated as…”

mentioning

confidence: 99%

Application of agent-based simulated annealing and tabu search procedures to solving the data reduction problem

Czarnowski¹,

Jędrzejowicz²

2011

International Journal of Applied Mathematics and Computer Science

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The problem considered concerns data reduction for machine learning. Data reduction aims at deciding which features and instances from the training set should be retained for further use during the learning process. Data reduction results in increased capabilities and generalization properties of the learning model and a shorter time of the learning process. It can also help in scaling up to large data sources. The paper proposes an agent-based data reduction approach with the learning process executed by a team of agents (A-Team). Several A-Team architectures with agents executing the simulated annealing and tabu search procedures are proposed and investigated. The paper includes a detailed description of the proposed approach and discusses the results of a validating experiment.

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“…Unlike traditional implementations [4], to find a good approximation of the quasiequilibrium condition, our approach is based on the fact that if a state can be sampled infinitely often in time (IOT) with probability (2), then an exponential decrease of the temperature still guarantees to find the global optimum. Moreover, we do not use an inhomogenous algorithm as in [3], in order to avoid settings of parameters which are problem dependent and difficult to set by the user.…”

Section: Simulated Annealingmentioning

confidence: 99%