An insulator pollution degree detection method based on crisscross optimization algorithm with blending ensemble learning

Zhang, Jianfeng; Wen, Huikang; Wang, Hongxing; Zhang, Guoxin; Wen, Huiting; Jiang, Huayang; Jiayu, Rong

doi:10.1109/ceect55960.2022.10030659

Cited by 1 publication

(5 citation statements)

References 14 publications

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“…Cuckoo has few search parameters, and the convergence rate is not sensitive to parameter changes, therefore it does not easily fall into local optimization and has a strong global search ability. However, its searchability is at the cost of a slow convergence rate, lacking vitality [ 21 ].…”

Section: Cs-ga-xgboostmentioning

confidence: 99%

“…The construction of CS is based on three ideal assumptions: (1) each cuckoo will only lay one egg at a time, and the nest will be randomly selected for laying eggs; (2) only produce the best eggs in the best nest; (3) the probability of foreign bird eggs being discovered is

, and after being discovered by the host, they will directly abandon the nest or bird eggs. Based on these three rules, the formula for updating the location and path of cuckoos searching for their nests is [ 21 ]:

where

represents the position of i -th nest at iteration t .

is the step size,

is the product,

follows the Levy distribution, essentially providing a random step size:

…”

Section: Cs-ga-xgboostmentioning

confidence: 99%

“…(8) The construction of CS is based on three ideal assumptions: (1) each cuckoo will only lay one egg at a time, and the nest will be randomly selected for laying eggs; (2) only produce the best eggs in the best nest; (3) the probability of foreign bird eggs being discovered is P ∈ [0,1], and after being discovered by the host, they will directly abandon The construction of CS is based on three ideal assumptions: (1) each cuckoo will only lay one egg at a time, and the nest will be randomly selected for laying eggs; (2) only produce the best eggs in the best nest; (3) the probability of foreign bird eggs being discovered is P ∈ [0, 1], and after being discovered by the host, they will directly abandon the nest or bird eggs. Based on these three rules, the formula for updating the location and path of cuckoos searching for their nests is [21]:…”

Section: Cs-gamentioning

confidence: 99%

“…Though CS has few parameters and its convergence rate is not sensitive to parameter changes, it does not easily fall into local optimization. However, its convergence is still slow and lacks vitality [ 21 ]. It can be seen that a single swarm intelligence optimization search technique has its shortcomings.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, this paper proposes a modeling method of CS-GA-XGBoost, integrating GA’s crossover operator into CS, making full use of the advantages of CS’s strong global search ability and GA’s fast rate of convergence [ 19 , 21 ], and applying it to XGBoost’s optimal parameter search, to achieve a more rapid rate of convergence and optimization ability. This paper validates the effectiveness of the proposed CS-GA-XGBoost using measured data of the 2.5-GHz-GaN class-E PA and 2.2–6.5-GHz CMOS PA under three different temperatures (−40 °C, 25 °C, and 125 °C).…”

Section: Introductionmentioning

confidence: 99%

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CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures

Wang,

Zhou

2023

Micromachines

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Machine learning methods, such as support vector regression (SVR) and gradient boosting, have been introduced into the modeling of power amplifiers and achieved good results. Among various machine learning algorithms, XGBoost has been proven to obtain high-precision models faster with specific parameters. Hyperparameters have a significant impact on the model performance. A traditional grid search for hyperparameters is time-consuming and labor-intensive and may not find the optimal parameters. To solve the problem of parameter searching, improve modeling accuracy, and accelerate modeling speed, this paper proposes a PA modeling method based on CS-GA-XGBoost. The cuckoo search (CS)-genetic algorithm (GA) integrates GA’s crossover operator into CS, making full use of the strong global search ability of CS and the fast rate of convergence of GA so that the improved CS-GA can expand the size of the bird nest population and reduce the scope of the search, with a better optimization ability and faster rate of convergence. This paper validates the effectiveness of the proposed modeling method by using measured input and output data of 2.5-GHz-GaN class-E PA under different temperatures (−40 °C, 25 °C, and 125 °C) as examples. The experimental results show that compared to XGBoost, GA-XGBoost, and CS-XGBoost, the proposed CS-GA-XGBoost can improve the modeling accuracy by one order of magnitude or more and shorten the modeling time by one order of magnitude or more. In addition, compared with classic machine learning algorithms, including gradient boosting, random forest, and SVR, the proposed CS-GA-XGBoost can improve modeling accuracy by three orders of magnitude or more and shorten modeling time by two orders of magnitude, demonstrating the superiority of the algorithm in terms of modeling accuracy and speed. The CS-GA-XGBoost modeling method is expected to be introduced into the modeling of other devices/circuits in the radio-frequency/microwave field and achieve good results.

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Section: Cs-ga-xgboostmentioning

confidence: 99%

where

represents the position of i -th nest at iteration t .

is the step size,

is the product,

follows the Levy distribution, essentially providing a random step size:

…”

Section: Cs-ga-xgboostmentioning

confidence: 99%

Section: Cs-gamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures

Wang,

Zhou

2023

Micromachines

View full text Add to dashboard Cite

show abstract

An insulator pollution degree detection method based on crisscross optimization algorithm with blending ensemble learning

Cited by 1 publication

References 14 publications

CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures

CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures

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