Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid Wolf Pack Algorithm and Particle Swarm Optimization

Li, Zhen; Yang, Liu; Wang, Dongsheng; Wei, Zheng

doi:10.1109/access.2020.2972826

Cited by 32 publications

(19 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PSO algorithm in its classical form has proven to be efficient for solving complex problems [11,99], achieving an approximation of the particles to the optimum of the problem-that is, a fast convergence [52,100]. Even so, PSO has some drawbacks, as mentioned.…”

Section: Pso Applicationsmentioning

confidence: 99%

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

et al. 2022

View full text Add to dashboard Cite

Companies are constantly changing in their organization and the way they treat information. In this sense, relevant data analysis processes arise for decision makers. Similarly, to perform decision-making analyses, multi-criteria and metaheuristic methods represent a key tool for such analyses. These analysis methods solve symmetric and asymmetric problems with multiple criteria. In such a way, the symmetry transforms the decision space and reduces the search time. Therefore, the objective of this research is to provide a classification of the applications of multi-criteria and metaheuristic methods. Furthermore, due to the large number of existing methods, the article focuses on the particle swarm algorithm (PSO) and its different extensions. This work is novel since the review of the literature incorporates scientific articles, patents, and copyright registrations with applications of the PSO method. To mention some examples of the most relevant applications of the PSO method; route planning for autonomous vehicles, the optimal application of insulin for a type 1 diabetic patient, robotic harvesting of agricultural products, hybridization with multi-criteria methods, among others. Finally, the contribution of this article is to propose that the PSO method involves the following steps: (a) initialization, (b) update of the local optimal position, and (c) obtaining the best global optimal position. Therefore, this work contributes to researchers not only becoming familiar with the steps, but also being able to implement it quickly. These improvements open new horizons for future lines of research.

show abstract

Section: Pso Applicationsmentioning

confidence: 99%

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Hybrid algorithms with a higher degree of integration of the rules and principles of operation of two algorithms are also possible: FireFly + PSO algorithm [14], PSO + artificial bee colony algorithm [15], PSO + ant colony optimization (ACO) [16], PSO + wolf pack algorithm [17]. This method allows us to create an algorithm that considers problem's features being solved and compensates for the shortcomings of one algorithm by adding properties of another to it.…”

Section: Issn: 2252-8938mentioning

confidence: 99%

“…Hybrids from different metaheuristic algorithms turn out to be even more complex and less universal. However, as shown in [17], the metaheuristic algorithm's efficiency can sometimes be increased on the contrary, by simplifying it. Perhaps that is why there are many works in which complex hybrid algorithms are successfully applied to solve a specific problem.…”

Section: Issn: 2252-8938mentioning

confidence: 99%

Generalized swarm intelligence algorithms with domain-specific heuristics

Matrenin

Myasnichenko

Sdobnyakov

et al. 2021

IJ-AI

View full text Add to dashboard Cite

<span lang="EN-US">In recent years, hybrid approaches on population-based algorithms are more often applied in industrial settings. In this paper, we present the approach of a combination of universal, problem-free Swarm Intelligence (SI) algorithms with simple deterministic domain-specific heuristic algorithms. The approach focuses on improving efficiency by sharing the advantages of domain-specific heuristic and swarm algorithms. A heuristic algorithm helps take into account the specifics of the problem and effectively translate the positions of agents (particle, ant, bee) into the problem's solution. And a Swarm algorithm provides an increase in the adaptability and efficiency of the approach due to stochastic and self-organized properties. We demonstrate this approach on two non-trivial optimization tasks: scheduling problem and finding the minimum distance between 3D isomers.</span>

show abstract

“…Miranda et al [26] con-sidered the uncertainty of stochastic equipment data in power system expansion planning, and calculated uncertainty by using interval arithmetic through the theory of imprecise probabilities. The parameters of the software reliability model were estimated and predicted by Zhen et al [27] using the hybrid WPA-PSO algorithm. Wang et al [28] carried out point estimation and approximate interval estimation of model parameters for Kijima type Weibull generalized renewal processes models I and II, and proposed a calculation method of the reliability indices for repairable systems with imperfect repair.…”

Section: Introductionmentioning

confidence: 99%

Reliability Study of Parameter Uncertainty Based on Time-Varying Failure Rates with an Application to Subsea Oil and Gas Production Emergency Shutdown Systems

Zuo

Yin

et al. 2021

Processes

View full text Add to dashboard Cite

The failure rate of equipment during long-term operation in severe environment is time-varying. Most studies regard the failure rate as a constant, ignoring the reliability evaluation error caused by the constant. While studying failure data that are few and easily missing, it is common to focus only on the uncertainty of reliability index rather than parameter of failure rate. In this study, a new time-varying failure rate model containing time-varying scale factor is established, and a statistical-fuzzy model of failure rate cumulated parameter is established by using statistical and fuzzy knowledge, which is used to modify the time-varying failure rate model. Subsequently, the theorem of the upper boundary existence for the failure rate region is proposed and proved to provide the failure rate cumulated parameter when the failure rate changes the fastest. The proposed model and theorem are applied to analyze the reliability of subsea emergency shutdown system in the marine environment for a long time. The comparison of system reliability under time-varying failure rate and constant failure rate shows that the time-varying failure rate model can eliminate the evaluation error and is consistent with engineering. The reliability intervals based on the failure rate model before and after modification are compared to analyze differences in uncertainty, which confirm that the modified model is more accurate and more practical for engineering.

show abstract

Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid Wolf Pack Algorithm and Particle Swarm Optimization

Cited by 32 publications

References 17 publications

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

Generalized swarm intelligence algorithms with domain-specific heuristics

Reliability Study of Parameter Uncertainty Based on Time-Varying Failure Rates with an Application to Subsea Oil and Gas Production Emergency Shutdown Systems

Contact Info

Product

Resources

About