Salp Swarm Optimizer for Modeling Software Reliability Prediction Problems

Kassaymeh, Sofian; Abdullah, Salwani; Al-Laham, Mohamad; Alweshah, Mohammed; Al-Betar, Mohammed Azmi; Othman, Zalinda

doi:10.1007/s11063-021-10607-6

Cited by 25 publications

(8 citation statements)

References 94 publications

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“…In this study, experimentation (testing DNN techniques with different hidden layers, epochs, neurons, and activation functions), the study problem, dataset size, processing time, and computing power were used to find the best combination of hyperparameters. The research community has shown that using more than one hidden layer can be fruitful in solving the prediction problem [65]. When the number of neurons and epochs increased, the loss decreased, and the accuracy increased [66].…”

Section: Implementing the Proposed Optimized Approachmentioning

confidence: 99%

An Optimized Deep Learning Approach for Improving Airline Services

Ouf¹

2023

Computers, Materials &Amp; Continua

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The aviation industry is one of the most competitive markets. The most common approach for airline service providers is to improve passenger satisfaction. Passenger satisfaction in the aviation industry occurs when passengers' expectations are met during flights. Airline service quality is critical in attracting new passengers and retaining existing ones. It is crucial to identify passengers' pain points and enhance their satisfaction with the services offered. The airlines used a variety of techniques to improve service quality. They used data analysis approaches to analyze the passenger point data. These solutions have focused simply on surveys; consequently, deeplearning approaches have received insufficient attention. In this study, deep neural networks with the adaptive moment estimation Adam optimization algorithm were applied to enhance classification performance. In previous studies, the quality of the dataset has been ignored. The proposed approach was applied to the airline passenger satisfaction dataset from the Kaggle repository. It was validated by applying artificial neural networks (ANNs), random forests, and support vector machine techniques to the same dataset. It was compared with other research papers that used the same dataset and had a similar problem. The experimental results showed that the proposed approach outperformed previous studies. It has achieved an accuracy of 99.3%.

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Section: Implementing the Proposed Optimized Approachmentioning

confidence: 99%

An Optimized Deep Learning Approach for Improving Airline Services

Ouf¹

2023

Computers, Materials &Amp; Continua

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“…In a more recent study, Kassaymeh et al [10] proposed a hybrid model for software reliability prediction by integrating the salp swarm algorithm (SSA) with backpropagation neural network (BPNN) to determine the optimal weights of the network. After a comparative study based on various performance measures, they concluded that the hybrid model SSA-BPNN performed better than the BPNN.…”

Section: Rani and Mahapatramentioning

confidence: 99%

“…RVM requires more training time than SVM because of the optimization of a non-convex function, but it does not require the use of free parameters. Regardless of the advantages of RVM over SVM, it requires the selection of a number of kernel parameters that vary according to the type of kernel used [10].…”

Section: Rani and Mahapatramentioning

confidence: 99%

SOM-FTS: A Hybrid Model for Software Reliability Prediction and MCDM-Based Evaluation

Kumar

Kaur

2022

Int. j. eng. technol. innov.

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The objective of this study is to propose a hybrid model based on self-organized maps (SOM) and fuzzy time series (FTS) for predicting the reliability of software systems. The proposed SOM-FTS model is compared with eleven traditional machine learning-based models. The problem of selecting a suitable software reliability prediction model is represented as a multi-criteria decision-making (MCDM) problem. Twelve software reliability prediction models, including the proposed SOM-FTS model, are evaluated using three MCDM methods, four performance measures, and three software failure datasets. The results show that the proposed SOM-FTS model is the most suitable model among the twelve software reliability prediction models on the basis of MCDM ranking.

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“…By simulating the forage behaviour of salp chains, Mirjalili (2017) proposed a novel optimization algorithm, salp swarm algorithm (SSA), which has advantages of fewer control parameters and better flexibility. It provides an effective technique for solving increasingly complex optimization problems, and has been successfully used in a variety of fields, such as feature selection (Chamchuen et al 2021;Balakrishnan et al 2021), economic load dispatch problem (Saha et al 2021), production cost assessment (Saravanan et al 2021), image segmentation (Houssein et al 2021;Dhabal et al 2020) and software fault prediction (Kassaymeh et al 2021). However, similar to other nature-inspired swarm intelligent algorithms, SSA has to overcome the shortcomings of premature convergence and low convergence rate.…”

Section: Introductionmentioning

confidence: 99%

A hybrid multiobjective salp swarm algorithm enhanced with sine cosine operator and normal cloud model

Tang

Ri-zhao

Liu

2023

Preprint

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As a nature-inspired metaheuristic algorithm, salp swarm algorithm (SSA) still suffers from low searching efficiency and easily falling into local optimum, especially when solving composite problem. In order to enhance the performance of SSA, an improved SSA equipped with sine cosine algorithm and normal cloud generator (CSCSSA) is proposed in this paper. The sine and cosine operator can prevent the salp leader from ineffective search for possible food position, and speed up the search rate of SSA. In addition, the normal cloud generator is employed to replace the position update mechanismof salp followers, and enhance the diversity by generating cloud drops around the salp leader. Comprehensive comparison of CSCSSA and seven other optimization algorithms was conducted on CEC2017 benchmark functions. The statistical results and convergence curves prove that the CSCSSA can be considered as highly competitive algorithm according to the searching efficiency, convergence accuracy and the ability of local optimum avoidance.

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Salp Swarm Optimizer for Modeling Software Reliability Prediction Problems

Cited by 25 publications

References 94 publications

An Optimized Deep Learning Approach for Improving Airline Services

An Optimized Deep Learning Approach for Improving Airline Services

SOM-FTS: A Hybrid Model for Software Reliability Prediction and MCDM-Based Evaluation

A hybrid multiobjective salp swarm algorithm enhanced with sine cosine operator and normal cloud model

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