Advanced a New Vector Data Query Optimization Method Based on Cost Function

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

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Genetic Algorithm and its Application in Optimization of Frequency Selective Surface

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.143-144.1046 ◽

2010 ◽

Vol 143-144 ◽

pp. 1046-1050

Author(s):

Jing Yu Han ◽

Wang Qun ◽

Chuan You Li ◽

Zhang Hong Tang ◽

Mei Wu Shi

Keyword(s):

Genetic Algorithm ◽

Cost Function ◽

Electroless Plating ◽

Genetic Basis ◽

Optimization Method ◽

Frequency Selective Surface ◽

Test System ◽

Genetic Algorithm Method ◽

Frequency Selective ◽

Test Result

In this paper, a new genetic algorithm method to optimize the frequency selective surface(FSS) is presented. The optimization speed and definition are promoted by limiting the parameter range and changing the genetic basis. A new cost function is introduced to optimize the multi-frequency of FSS by multi-object optimization (MO). The cirque element was optimized by the optimization method, fabricated by the selective electroless plating on fabric and measured by the arch test system. Test result proves the simulated result coincide with measured result. Result shows that it’s possible to realize different optimizations based on the various applying by this method.

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Optimization Method of Massive Data Query

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.3247 ◽

2014 ◽

Vol 602-605 ◽

pp. 3247-3250

Author(s):

Yu Ming Chen

Keyword(s):

Optimization Method ◽

Experimental Results ◽

Massive Data ◽

Information Complexity ◽

Apriori Algorithm ◽

Amount Of Information ◽

Data Query ◽

Target Data

Optimization method ofmassive dataquery is researched in this paper.In the modernInternet environment,the datahas the characteristics oflarge amount of information, complexity, disorder, andchaosassociation. Using traditionalqueried methodsoftenrequirea lot oflimitedconditions, witha lot of drawbacks such as time-consuming data query, moreineffective queryand low efficiency.To this end, anoptimizationmethod of massive data query based onparallel Apriori algorithm is proposed in this paper.The massive dataare made simplification processing andredundant data are deleted to providedata foundation for fast and accuratedataquery.Effectiveassociation rulesof the massive data are calculated, in order to obtain the relevantof the target data. Based onAprioriparallel algorithm,massivedata are processedto achieveaccurate query. Experimental results show thatthe use ofoptimization algorithm for massive dataquerycan improvethe query speedof target data and it has a strong superiority.

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Convergence of Simulated Annealing with Feedback Temperature Schedules

Probability in the Engineering and Informational Sciences ◽

10.1017/s0269964800004836 ◽

1997 ◽

Vol 11 (3) ◽

pp. 279-304 ◽

Cited By ~ 4

Author(s):

M. Kolonko ◽

M. T. Tran

Keyword(s):

Simulated Annealing ◽

Cost Function ◽

Job Shop ◽

Job Shop Scheduling ◽

Optimization Method ◽

Search Process ◽

Local Optimum ◽

Fixed Sequence ◽

Temperature Parameter ◽

The Cost

It is well known that the standard simulated annealing optimization method converges in distribution to the minimum of the cost function if the probability a for accepting an increase in costs goes to 0. α is controlled by the “temperature” parameter, which in the standard setup is a fixed sequence of values converging slowly to 0. We study a more general model in which the temperature may depend on the state of the search process. This allows us to adapt the temperature to the landscape of the cost function. The temperature may temporarily rise such that the process can leave a local optimum more easily. We give weak conditions on the temperature schedules such that the process of solutions finally concentrates near the optimal solutions. We also briefly sketch computational results for the job shop scheduling problem.

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