Forecasting of software reliability using neighborhood fuzzy particle swarm optimization based novel neural network

Roy, Pratik; Mahapatra, G. S.; Dey, Kashi Nath

doi:10.1109/jas.2019.1911753

Cited by 58 publications

(30 citation statements)

References 34 publications

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“…e value for random seed is explained specifically by the Gaussian random variables samples. (iii) irdly, the study of the nonparametric density estimation (18) with the normal kernel algorithm and multivariate density function have been generally completed to calculate the transitional density (17). (iv) en, it is necessary to repeat the above step for each time point t 0 , .…”

Section: Simulated Maximum Likelihood Methodsmentioning

confidence: 99%

“…PSO characterized as an artificial intelligence (AI) method is used to make a process of approximation of the minimization problems, which is a sort of nondifferentiable optimization problem in order to arrive at a solution. A number of comparison studies have been conducted to investigate the efficiency of PSO and GA [17][18][19][20][21][22][23]. Also, particle swarm optimization provides an important way in fine-tuning the parameters of finance models and deserved popularity in this field [24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Particle Swarm Optimization Algorithm to Calibrate the Term Structure Model

Zhou

Liu

Tian

et al. 2021

Mathematical Problems in Engineering

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One of the advantages of stochastic differential equations (SDE) is that they can follow a variety of different trends so that they can establish complex dynamic systems in the economic and financial fields. Although some estimation methods have been proposed to identify the unknown parameters in virtue of the results in the SDE model to speed up the process, these solutions only focus on using explicit approach to solve SDEs, and therefore they are not reliable to deal with data source merged being large and varied. Thus, this study makes progress in creating a new implicit way to fill in the gaps of accurately calibrating the unknown parameters in the SDE model. Essentially, the primary goal of the article is to generate rigid SDE simulation. Meanwhile, the particle swarm optimization method serves a purpose to search and simultaneously obtain the optimal estimation of the model unknown parameters in the complicated experiment of parameter space in an effective way. Finally, in an interest rate term structure model, it is verified that the method effectively deals with parameter estimation in the SDE model.

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Section: Simulated Maximum Likelihood Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Particle Swarm Optimization Algorithm to Calibrate the Term Structure Model

Zhou

Liu

Tian

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…And some research studies [39,40] also applied more advanced particle swarm optimization variants in the neural network, burden distribution matrix, and other aspects.…”

Section: Solution Methodologymentioning

confidence: 99%

Collaborative Multidepot Petrol Station Replenishment Problem with Multicompartments and Time Window Assignment

Wang

et al. 2020

Journal of Advanced Transportation

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Energy supply is an important system that affects the overall efficiency of urban transportation. To improve the system operational efficiency and reduce costs, we formulate and solve a collaborative multidepot petrol station replenishment problem with multicompartments and time window assignment by establishing a mixed-integer linear programming model. The hybrid heuristic algorithm composed of genetic algorithm and particle swarm optimization is used as a solution, and then the Shapley value method is applied to analyze the profit allocation of each petrol depot under different coalitions. The optimal membership sequence of the cooperation is determined according to the strict monotone path. To analyze and verify the effectiveness of the proposed method, a regional petrol supply network in Chongqing city in China is investigated. Through cooperation between petrol depots in the supply network, the utilization of customer clustering, time window coordination, and distribution truck sharing can significantly reduce the total operation costs and improve the efficiency of urban transportation energy supply. This approach can provide theoretical support for relevant government departments and enterprises to make optimal decisions. The implementation of the joint distribution of energy can promote the sustainable development of urban transportation.

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“…The CSO is a kind of group intelligence that improves PSO to face large-scale classification problems. It is a mechanism for comparing the evaluation results of different particles selected from the population; only the failed particles are learned to update [45]. Therefore, in addition to the number of updated particles being able to be reduced to 2/N, the excellent solutions in the search do not need to be saved, and it can be used for efficient search on large-scale classification problems.…”

Section: Competitive Swarm Optimizermentioning

confidence: 99%

Validation of Large-Scale Classification Problem in Dendritic Neuron Model Using Particle Antagonism Mechanism

Jia

Fujishita²,

et al. 2020

Electronics

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With the characteristics of simple structure and low cost, the dendritic neuron model (DNM) is used as a neuron model to solve complex problems such as nonlinear problems for achieving high-precision models. Although the DNM obtains higher accuracy and effectiveness than the middle layer of the multilayer perceptron in small-scale classification problems, there are no examples that apply it to large-scale classification problems. To achieve better performance for solving practical problems, an approximate Newton-type method-neural network with random weights for the comparison; and three learning algorithms including back-propagation (BP), biogeography-based optimization (BBO), and a competitive swarm optimizer (CSO) are used in the DNM in this experiment. Moreover, three classification problems are solved by using the above learning algorithms to verify their precision and effectiveness in large-scale classification problems. As a consequence, in the case of execution time, DNM + BP is the optimum; DNM + CSO is the best in terms of both accuracy stability and execution time; and considering the stability of comprehensive performance and the convergence rate, DNM + BBO is a wise choice.

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Forecasting of software reliability using neighborhood fuzzy particle swarm optimization based novel neural network

Cited by 58 publications

References 34 publications

Using Particle Swarm Optimization Algorithm to Calibrate the Term Structure Model

Using Particle Swarm Optimization Algorithm to Calibrate the Term Structure Model

Collaborative Multidepot Petrol Station Replenishment Problem with Multicompartments and Time Window Assignment

Validation of Large-Scale Classification Problem in Dendritic Neuron Model Using Particle Antagonism Mechanism

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