An Improved NSGA-III Algorithm Based on Deep Q-Networks for Cloud Storage Optimization of Blockchain

Zhou, Yu; Ren, Yanli; Xu, Mengtian; Feng, Guorui

doi:10.1109/tpds.2023.3243634

Cited by 4 publications

(1 citation statement)

References 64 publications

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“…NSGA3 (Zhou et al, 2023) is an another important component of EANDL. The t-th generation of NSGA3 is generated as shown below.…”

Section: Structure Of Eandlmentioning

confidence: 99%

EANDL: A High-performance and Low-cost Effort-Aware Just-in-Time Software Defect Prediction Model

Chen,

Wang,

Song

et al. 2023

Preprint

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Just-in-time software defect prediction (JIT-SDP) is a fine-grained software defect prediction technology and its predictive entity is code changes. Effort-aware just-in-time software defect prediction (Effort-aware JIT-SDP) is a defect prediction technology that takes the cost of detection into consideration with the goal of detecting more defect changes with less cost. To solve the problem of poor prediction performance of Effort aware JIT-SDP model, an Effort-aware JIT-SDP model called EANDL based on NSGA3, Differential Evolution and Logistic Regression is proposed. Firstly, two evaluation metrics called DPR and LDSOD are proposed to evaluate the sorting performance. Secondly, the prediction model is constructed by using logistic regression and maximizing two evaluation metrics using NSGA3-DE so as to determine the optimal coefficient vector of logistic regression. In addition, two metrics called ACE and ACEP to measure the effort consumption of Effort-aware JIT-SDP models are proposed. Finally, intensive simulation experiments have been conducted on six open-source datasets to demonstrate the performance of the proposed method. Empirical results demonstrate that EANDL outperforms eight typical and classical supervised and unsupervised models in terms of sorting performance, effort consumption, and response time.

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“…NSGA3 (Zhou et al, 2023) is an another important component of EANDL. The t-th generation of NSGA3 is generated as shown below.…”

Section: Structure Of Eandlmentioning

confidence: 99%

EANDL: A High-performance and Low-cost Effort-Aware Just-in-Time Software Defect Prediction Model

Chen,

Wang,

Song

et al. 2023

Preprint

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A dual distance dominance based evolutionary algorithm with selection-replacement operator for many-objective optimization

Zhang,

Liu,

Liu

et al. 2024

Expert Systems with Applications

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Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities

Song,

Wu,

Guo

et al. 2024

Swarm and Evolutionary Computation

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An Improved NSGA-III Algorithm Based on Deep Q-Networks for Cloud Storage Optimization of Blockchain

Cited by 4 publications

References 64 publications

EANDL: A High-performance and Low-cost Effort-Aware Just-in-Time Software Defect Prediction Model

EANDL: A High-performance and Low-cost Effort-Aware Just-in-Time Software Defect Prediction Model

A dual distance dominance based evolutionary algorithm with selection-replacement operator for many-objective optimization

Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities

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