A novel approach of NPSO on dynamic weighted NHPP model for software reliability analysis with additional fault introduction parameter

Rani, Pooja; Mahapatra, G. S.

doi:10.1016/j.heliyon.2019.e02082

Cited by 9 publications

(5 citation statements)

References 54 publications

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“…Species with strong survival ability stay in the microhabitat, while those with weak survival ability are eliminated, and under this mechanism of "survival of the fittest," species in the microhabitat evolve. Using the microhabitat technique, each generation of individuals is divided into several classes, and a number of individuals with greater adaptability in each class are selected as the best representatives of a class to form a swarm, which dynamically forms a relatively independent search space to achieve simultaneous search of multiple extremal regions, in order to overcome the defects of early convergence and easy to fall into local optimum of the basic particle swarm algorithm, and obtain better recognition accuracy and convergence speed (Lu and Li, 2019;Rani and Mahapatra, 2019). Thus, this paper adopts the improved small habitat particle swarm algorithm with high reasonableness and feasibility.…”

Section: Solution Of the Lower Modelmentioning

confidence: 99%

RETRACTED: Bi-Level Optimization Dispatch of Integrated-Energy Systems With P2G and Carbon Capture

Zhang

et al. 2022

Front. Energy Res.

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The power-to-gas (P2G) technology transforms the unidirectional coupling of power network and natural gas network into bidirectional coupling, and its operational characteristics provide an effective way for wind and solar energy accommodation. The paper proposes a bi-level optimal dispatch model for the integrated energy system with carbon capture system and P2G facility. The upper model is an optimal allocation model for coal-fired units, and the lower model is an economic dispatch model for the integrated energy system. Moreover, the upper model is solved by transforming the model into a mixed-integer linear programming problem and calling CPLEX, and the lower model is a multi-objective planning problem, which is solved by improving the small-habitat particle swarm algorithm. Finally, the simulation is validated by the MATLAB platform, and the results show that the simultaneous consideration of carbon capture system and P2G facility improves the economics of the integrated energy system and the capacity of wind and solar energy accommodation.

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Section: Solution Of the Lower Modelmentioning

confidence: 99%

RETRACTED: Bi-Level Optimization Dispatch of Integrated-Energy Systems With P2G and Carbon Capture

Zhang

et al. 2022

Front. Energy Res.

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“…Figure 2a, b are diagrammatic representations of the Standard PSO algorithm and the proposed PSO algorithm respectively. The ubiquitous ring topology (Rani & Mahapatra, 2019) is used as the configuration for deriving the neighbourhood best. Consequently, the velocity Equation (2) can be modified to yield Equation ().

V \frac{0pt}{t + 1} goodbreak= italicωV \frac{0pt}{i} goodbreak+ φ_{1} r_{1} ((), {pb}_{id} goodbreak- X \frac{0pt}{t}) goodbreak+ φ_{2} r_{2} ((), {gb}_{d} goodbreak- X \frac{0pt}{t}) goodbreak+ φ_{3} r_{3} ((), {nb}_{d} goodbreak- X \frac{0pt}{t})

…”

Section: Proposed Methodologymentioning

confidence: 99%

“…T The comparative study of the average performance (10 runs with 100 iterations each) of the proposed EBPSO along with other PSO variants MBPSO (Zhang et al, 2014), IDE-PSO (Gou et al, 2017), and NPSO (Rani & Mahapatra, 2019) with a k-NN classifier as per t-he parameters given in Table 2 is presented in Table 6. It shows that the EBPSO gives remarkable average classification accuracy as compared to other PSO variants.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

EBPSO: Enhanced binary particle swarm optimization for cardiac disease classification with feature selection

Wadhawan

Maini

2022

Expert Systems

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Cardiac disease is one of the leading causes of death worldwide, and its early detection and diagnosis can considerably increase the lifespan of patients. An automated expert system for early and accurate diagnosis that offsets human error can be designed using machine intelligence and appropriate pre-processing of data to ensure accuracy. To that end, enhanced binary particle swarm optimization (EBPSO) has been investigated in this paper to enable the definitive classification of cardiac disease with the aid of feature selection. The primary objective of this study is to improve the accuracy of the classification and the convergence speed while employing a lesser number of features. In order to achieve this, a new adaptive inertia weight was introduced to balance the exploitation and exploration capability of binary particle swarm optimization (BPSO). In addition, a novel idea called 'neighbourhood best' is introduced in the velocity update equation to improve the convergence speed. The proposed EBPSO approach was tested over well-known datasets of heart disease, that is, the Cleveland dataset, Hungarian dataset, Switzerland dataset, and Long-Beach-Va datasets. For classification purposes, two well-known classifiers, k-nearest neighbour, and support vector machine were used with the EBPSO. Furthermore, the performance of EBPSO was compared with the traditional BPSO, as well as with BPSO with proposed inertia weight (W a -BPSO) and other PSO variants. It has been observed that the proposed approach EBPSO with KNN shows remarkable efficiency in terms of average classification accuracy and convergence rate. The average classification accuracy for Cleveland dataset is 92.814%, for the Hungarian dataset is 91.97%, for the Switzerland dataset is 91.626% with EBPSO+KNN and for the Long-Beach-Va dataset is 85.253% with EBPSO + SVM. Additionally, the performance of W a -BPSO was compared with other commonly used inertia weights. The results of this study demonstrated that the proposed approach produces better results in terms of both the accuracy of classification as well as the convergence rate with a reduced feature set.

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“…Rani [15] proposed a neural network approach focused on predictions of software reliability. He compared the approach to parametric model recalibration with some meaningful predictive measures with the same data sets.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Quartile deviation-based Support vector regression model for software reliability datasets

Y¹

2020

IJATCSE

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Software reliability prediction models are used to predict the fault rate of the software systems using machine learning models. A large number of traditional reliability measures are used to test the software faults in the debugging and testing process. Most of the traditional machine learning based fault prediction models are integrated with standard software reliability growth measures for reliability severity classification. However, these models are used to predict the reliability level of binary class with less standard error. In this paper, a hybrid support vector regression-based quartile deviation growth measure is implemented on the training fault datasets. Experimental results are simulated on various reliability datasets with different configuration parameters for fault prediction.

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A novel approach of NPSO on dynamic weighted NHPP model for software reliability analysis with additional fault introduction parameter

Cited by 9 publications

References 54 publications

RETRACTED: Bi-Level Optimization Dispatch of Integrated-Energy Systems With P2G and Carbon Capture

RETRACTED: Bi-Level Optimization Dispatch of Integrated-Energy Systems With P2G and Carbon Capture

EBPSO: Enhanced binary particle swarm optimization for cardiac disease classification with feature selection

A Hybrid Quartile deviation-based Support vector regression model for software reliability datasets

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