Cost Optimization of SRGM using Genetic Algorithm

Saraswat, Manish

doi:10.5120/ijca2016910266

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Figure 6 shows the data shape of the input and output data. To train and test the deep learning model, When initiating the GA algorithm, population initialization is necessary [30]. Figure 8 illustrates the exact execution process.…”

Section: Processing Datamentioning

confidence: 99%

MuSRGM: A Genetic Algorithm-Based Dynamic Combinatorial Deep Learning Model for Software Reliability Engineering

FU,

RYU,

KIM

2024

IEICE Trans. Inf. & Syst.

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In the software testing phase, software reliability growth models (SRGMs) are commonly used to evaluate the reliability of software systems. Traditional SRGMs are restricted by their assumption of a continuous growth pattern for the failure detection rate (FDR) throughout the testing phase. However, the assumption is compromised by Change-Point phenomena, where FDR fluctuations stem from variations in testing personnel or procedural modifications, leading to reduced prediction accuracy and compromised software reliability assessments. Therefore, the objective of this study is to improve software reliability prediction using a novel approach that combines genetic algorithm (GA) and deep learning-based SRGMs to account for the Change-point phenomenon. The proposed approach uses a GA to dynamically combine activation functions from various deep learning-based SRGMs into a new mutated SRGM called MuSRGM. The MuSRGM captures the advantages of both concave and S-shaped SRGMs and is better suited to capture the change-point phenomenon during testing and more accurately reflect actual testing situations. Additionally, failure data is treated as a time series and analyzed using a combination of Long Short-Term Memory (LSTM) and Attention mechanisms. To assess the performance of MuSRGM, we conducted experiments on three distinct failure datasets. The results indicate that MuSRGM outperformed the baseline method, exhibiting low prediction error (MSE) on all three datasets. Furthermore, MuSRGM demonstrated remarkable generalization ability on these datasets, remaining unaffected by uneven data distribution. Therefore, MuSRGM represents a highly promising advanced solution that can provide increased accuracy and applicability for software reliability assessment during the testing phase.

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Section: Processing Datamentioning

confidence: 99%

MuSRGM: A Genetic Algorithm-Based Dynamic Combinatorial Deep Learning Model for Software Reliability Engineering

FU,

RYU,

KIM

2024

IEICE Trans. Inf. & Syst.

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“…Lead time optimization through crashing has been discussed for various construction, manufacturing, supply chain, and production management projects (Saraswat, 2016). Most of the studies discussed lead time optimization for construction and supply chain management projects.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Lead Time through Genetic Algorithm: A Case Study of Equipment Manufacturing Industry

Siddiqui

Shafiq

2021

JER is an international, peer-reviewed journal that publishes f

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This study aims at optimizing lead time through effective management of resources in an equipment manufacturing industry to ensure timely completion of the projects. The concept of “project crashing” was implemented to identify potential activities at production line of conventional two-roller machines. The developed model is optimized using modified Genetic Algorithm (GA). The results showed that the proposed model enabled to deliver products as per schedule without increasing planned cost. Sensitivity analyses were also conducted to find out the impact of various process parameters. From analyses, it was found that the proposed model facilitate the manufacturer in delivering the products at specified time with increasing the planned cost.

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Cost Optimization of SRGM using Genetic Algorithm

Cited by 2 publications

References 19 publications

MuSRGM: A Genetic Algorithm-Based Dynamic Combinatorial Deep Learning Model for Software Reliability Engineering

MuSRGM: A Genetic Algorithm-Based Dynamic Combinatorial Deep Learning Model for Software Reliability Engineering

Optimization of Lead Time through Genetic Algorithm: A Case Study of Equipment Manufacturing Industry

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