Software reliability prediction based on support vector regression with binary particle swarm optimization for model mining

Insanittaqwa, Vika Fitratunnany; Rochimah, Siti

doi:10.1109/isemantic.2016.7873855

2016 International Seminar on Application for Technology of Information and Communication (ISemantic) 2016

DOI: 10.1109/isemantic.2016.7873855

|View full text |Cite

Software reliability prediction based on support vector regression with binary particle swarm optimization for model mining

Vika Fitratunnany Insanittaqwa

Siti Rochimah

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Overview on Binary Optimization Using Swarm-Inspired Algorithms

et al. 2021

View full text Add to dashboard Cite

Swarm Intelligence is applied to optimisation problems due to its robustness, scalability, generality, and flexibility. Based on simple rules, simple reactive agents -swarm (e.g. fish, bird, and ant)directly or indirectly exchange information to find an optimal solution. Among multiple nature inspirations and versions, the dilemma of choosing proper swarm-based algorithms for each type of problem prevents their recurrent application. This scenario gets even more challenging when considering binary optimisation because of the absence of overview papers that assembles the trends, benefits and limitations of swarmbased techniques. Based on 403 scientific papers, we describe the basis of the leading binary swarm-based algorithms presenting their rationales, equations, pseudocodes, and descriptions of their applications to tackle this research gap. We also define a new classification based on the mechanism to update the solutions and the displacements, indicating that the Binary-Binary approach -binary decision variables and binary search space -is more efficient for binary optimisation in accuracy and computational cost.

show abstract

Overview on Binary Optimization Using Swarm-Inspired Algorithms

et al. 2021

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Software reliability prediction based on support vector regression with binary particle swarm optimization for model mining

Cited by 1 publication

References 17 publications

Overview on Binary Optimization Using Swarm-Inspired Algorithms

Overview on Binary Optimization Using Swarm-Inspired Algorithms

Contact Info

Product

Resources

About