An explanation of ordinal optimization: Soft computing for hard problems

Ho, Yu-Chi

doi:10.1016/s0020-0255(98)10056-7

Cited by 134 publications

(172 citation statements)

References 6 publications

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“…The OO theory is a novel approach to cope with the complex and difficult optimization problem and can significantly reduce the number of search samples of the huge feasible solution space formed by all discrete control variables [20,21]. The OO theory aims to obtain sufficiently good solutions with a high probability instead of finding the perfect optimal solutions to avoid time consuming calculation.…”

Section: Oo Theorymentioning

confidence: 99%

“…Because the crude evaluation model is much faster than the accurate evaluation, this approach can improve the overall solving efficiency for the complex problem. OO has been applied to optimization problems in the power system [20]. Fig.…”

Section: Oo Theorymentioning

confidence: 99%

“…2, five types of order performance curves (OPCs) can be roughly categorized in OO: Flat (many good designs), Steep (many bad designs), Bell (many mediocre designs), U-shape (either good or bad designs) and Neutral (uniformly distributed designs). The shape of the OPCs determines the number in the set to perform the accurate evaluation [20].…”

Section: Oo Theorymentioning

confidence: 99%

See 2 more Smart Citations

Ordinal Optimization Theory Based Planning for Clustered Wind Farms Considering the Capacity Credit

Wang¹,

Zhang²,

Chen³

et al. 2015

Journal of Electrical Engineering and Technology

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-Wind power planning aims to locate and size wind farms optimally. Traditionally, wind power planners tend to choose the wind farms with the richest wind resources to maximize the energy benefit. However, the capacity benefit of wind power should also be considered in large-scale clustered wind farm planning because the correlation among the wind farms exerts an obvious influence on the capacity benefit brought about by the combined wind power. This paper proposes a planning model considering both the energy and the capacity benefit of the wind farms. The capacity benefit is evaluated by the wind power capacity credit. The Ordinal Optimization (OO) Theory, capable of handling problems with non-analytical forms, is applied to address the model. To verify the feasibility and advantages of the model, the proposed model is compared with a widely used genetic algorithm (GA) via a modified IEEE RTS-79 system and the real world case of Ningxia, China. The results show that the diversity of the wind farm enhances the capacity credit of wind power.

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Section: Oo Theorymentioning

confidence: 99%

Section: Oo Theorymentioning

confidence: 99%

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Ordinal Optimization Theory Based Planning for Clustered Wind Farms Considering the Capacity Credit

Wang¹,

Zhang²,

Chen³

et al. 2015

Journal of Electrical Engineering and Technology

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“…Simulations suggest that the majority of applications have a Bell shape, which implies that the good-enough data distribution to search is neutral (neither too good nor too bad). The effect of the Bell shape on the final selection set S is described by Ho et al [9] (6) Specify the noise level. Normalize the observed performance in { ( ), 41,42, ,1000}…”

Section: Configuration Autotuning Proceduresmentioning

confidence: 99%

“…1. (8) Use a look-up table [9] to calculate the size, s, of the selection set S. OO theory guarantees that S contains at least k good-enough configuration parameter vectors with a probability no less than α.…”

Section: Configuration Autotuning Proceduresmentioning

confidence: 99%

Performance improvement of distributed systems by autotuning of the configuration parameters

Zhang

Cao

Liu

et al. 2011

Tinshhua Sci. Technol.

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Abstract:The performance of distributed computing systems is partially dependent on configuration parameters recorded in configuration files. Evolutionary strategies, with their ability to have a global view of the structural information, have been shown to effectively improve performance. However, most of these methods consume too much measurement time. This paper introduces an ordinal optimization based strategy combined with a back propagation neural network for autotuning of the configuration parameters. The strategy was first proposed in the automation community for complex manufacturing system optimization and is customized here for improving distributed system performance. The method is compared with the covariance matrix algorithm. Tests using a real distributed system with three-tier servers show that the strategy reduces the testing time by 40% on average at a reasonable performance cost.

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References

2016

Power System Optimization

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An explanation of ordinal optimization: Soft computing for hard problems

Cited by 134 publications

References 6 publications

Ordinal Optimization Theory Based Planning for Clustered Wind Farms Considering the Capacity Credit

Ordinal Optimization Theory Based Planning for Clustered Wind Farms Considering the Capacity Credit

Performance improvement of distributed systems by autotuning of the configuration parameters

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