Opposition Learning Based Improved Bee Colony Optimization (OLIBCO) Algorithm for Data Clustering Article Swipe
Srikanta Kumar Sahoo
,
Priyabrata Pattanaik
,
Mihir Narayan Mohanty
,
Dilip Kumar Mishra
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2023.0140429
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2023.0140429
Clustering of data in case of data mining has a major role in recent research as well as data engineers. It supports for classification and regression type of problems. It needs to obtain the optimized clusters for such application. The partitional clustering and meta-heuristic search techniques are two helpful tools for this task. However the convergence rate is one of the important factors at the time of optimization. In this paper, authors have taken a data clustering approach with improved bee colony algorithm and opposition based learning to improve the rate of convergence and quality of clustering. It introduces the opposite bees that are created using opposition based learning to achieve better exploration. These opposite bees occupy exactly the opposite position that of the mainstream bees in the solution space. Both the mainstream and opposite bees explore the solution space together with the help of Bee Colony Optimization based clustering algorithm. This boosts the explorative power of the algorithm and hence the convergence rate. The algorithm uses a steady state selection procedure as a tool for exploration. The crossover and mutation operation is used to get balanced exploitations. This enables the algorithm to avoid sticking in local optima. To justify the effectiveness of the algorithm it is verified with the open datasets from the UCI machine learning repository as the benchmark. The simulation result shows that it performs better than some benchmark as well as recently proposed algorithms in terms of convergence rate, clustering quality, and exploration and exploitation capability.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14569/ijacsa.2023.0140429
- http://thesai.org/Downloads/Volume14No4/Paper_29-Opposition_Learning_Based_Improved_Bee_Colony_Optimization.pdf
- OA Status
- diamond
- Cited By
- 6
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4368275393
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4368275393Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.14569/ijacsa.2023.0140429Digital Object Identifier
- Title
-
Opposition Learning Based Improved Bee Colony Optimization (OLIBCO) Algorithm for Data ClusteringWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Srikanta Kumar Sahoo, Priyabrata Pattanaik, Mihir Narayan Mohanty, Dilip Kumar MishraList of authors in order
- Landing page
-
https://doi.org/10.14569/ijacsa.2023.0140429Publisher landing page
- PDF URL
-
https://thesai.org/Downloads/Volume14No4/Paper_29-Opposition_Learning_Based_Improved_Bee_Colony_Optimization.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://thesai.org/Downloads/Volume14No4/Paper_29-Opposition_Learning_Based_Improved_Bee_Colony_Optimization.pdfDirect OA link when available
- Concepts
-
Cluster analysis, Computer science, Benchmark (surveying), Crossover, Rate of convergence, Artificial bee colony algorithm, Machine learning, Local optimum, Data mining, Artificial intelligence, Algorithm, Mathematical optimization, Mathematics, Channel (broadcasting), Computer network, Geodesy, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 4Per-year citation counts (last 5 years)
- References (count)
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50Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W6632088959, https://openalex.org/W4223936435, https://openalex.org/W6682142637, https://openalex.org/W3090294925, https://openalex.org/W2887386074, https://openalex.org/W3122757008, https://openalex.org/W3021774969, https://openalex.org/W3211054247, https://openalex.org/W2751482852, https://openalex.org/W4285587860, https://openalex.org/W3087008130, https://openalex.org/W3126296655, https://openalex.org/W2811349597, https://openalex.org/W3129245968, https://openalex.org/W2805666699, https://openalex.org/W2066555182, https://openalex.org/W3113303646, https://openalex.org/W2976125381, https://openalex.org/W2921793854, https://openalex.org/W2865350967, https://openalex.org/W2898658722, https://openalex.org/W3008681130, https://openalex.org/W6631638067, https://openalex.org/W2143560894, https://openalex.org/W3120937314, https://openalex.org/W4288068170, https://openalex.org/W2774324652, https://openalex.org/W3068363330, https://openalex.org/W3036890924, https://openalex.org/W2926960342, https://openalex.org/W2944193307, https://openalex.org/W2790613404, https://openalex.org/W2041905867, https://openalex.org/W2023144546, https://openalex.org/W2007005198, https://openalex.org/W2038636021, https://openalex.org/W4321392396, https://openalex.org/W2251432411, https://openalex.org/W2162745921, https://openalex.org/W3012467882, https://openalex.org/W2518010140, https://openalex.org/W6680699249, https://openalex.org/W1639032689, https://openalex.org/W2140405352, https://openalex.org/W3023540311, https://openalex.org/W4292083457, https://openalex.org/W2140190241, https://openalex.org/W2011430131, https://openalex.org/W2769177808, https://openalex.org/W2153233077 |
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