The best-so-far ABC with multiple patrilines for clustering problems

Neurocomputing

Volumn 116, Issue , 2013, Pages 355-366

  Banharnsakun, Anan ^a   Sirinaovakul, Booncharoen ^a   Achalakul, Tiranee ^a  

^a KING MONGKUT'S UNIVERSITY OF TECHNOLOGY THONBURI   (Thailand)

Author keywords

Best so far ABC; Clustering; Distributed environments; Multiple patrilines; Optimization; Parallel computing; Swarm intelligence

Indexed keywords

BEST-SO-FAR ABC; CLUSTERING; DISTRIBUTED ENVIRONMENTS; MULTIPLE PATRILINES; SWARM INTELLIGENCE;

BIOINFORMATICS; EVOLUTIONARY ALGORITHMS; HEURISTIC METHODS; LEARNING SYSTEMS; OPTIMIZATION; PARALLEL ARCHITECTURES; PARALLEL PROCESSING SYSTEMS; PATTERN RECOGNITION; PROBLEM SOLVING;

CLUSTERING ALGORITHMS;

ALGORITHM; ARTICLE; ARTIFICIAL BEE COLONY; ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; CALCULATION; CLUSTER ANALYSIS; DATA ANALYSIS; DATA MINING; DECISION MAKING; IMAGE ANALYSIS; INFORMATION RETRIEVAL; MACHINE LEARNING; PRIORITY JOURNAL; PROCESS OPTIMIZATION; SWARM INTELLIGENCE;

EID: 84878496046     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.02.047     Document Type: Article

References (46)

1. Xu C., Duan H. Artificial bee colony (ABC) optimized edge potential function (EPF) approach to target recognition for low-altitude aircraft. Pattern Recognition Lett. 2010, 31:1759-1772.
2. Yeh W.-C., Chang W.-W., Chung Y.Y. A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method. Expert Syst. Appl. 2009, 36:8204-8211.
3. Fournier J.R.L., Pierre S. Assigning cells to switches in mobile networks using an ant colony optimization heuristic. Comput. Commun. 2005, 28:65-73.
4. Bella J.E., McMullen P.R. Ant colony optimization techniques for the vehicle routing problem. Adv. Eng. Inf. 2004, 18:41-48.
5. Ficko M., Brezovnik S., Klancnik S., Balic J., Brezocnik M., Pahole I. Intelligent design of an unconstrained layout for a flexible manufacturing system. Neurocomputing 2010, 73:639-647.
6. Zhang W., Liu J., Niu Y. Quantitative prediction of MHC-II binding affinity using particle swarm optimization. Artif. Intell. Med. 2010, 50:127-132.
7. Fan S., Chen L., Lee W.-J. Machine learning based switching model for electricity load forecasting. Energy Convers. Manage. 2008, 49:1331-1344.
8. Anaya A.R., Boticario J.G. Application of machine learning techniques to analyse student interactions and improve the collaboration process. Expert Syst. Appl. 2011, 38:1171-1181.
9. Yun C.-H., Chuang K.-T., Chen M.-S. Adherence clustering: an efficient method for mining market-basket clusters. Inf. Syst. 2006, 31:170-186.
10. Ci S., Guizani M., Sharif H. Adaptive clustering in wireless sensor networks by mining sensor energy data. Comput. Commun. 2007, 30:2968-2975.
11. Yuan T., Kuo W. Spatial defect pattern recognition on semiconductor wafers using model-based clustering and Bayesian inference. Eur. J. Oper. Res. 2008, 190:228-240.
12. Bassiou N., Kotropoulos C. Long distance bigram models applied to word clustering. Pattern Recognition 2011, 44:145-158.
13. Das S., Konar A. Automatic image pixel clustering with an improved differential evolution. Appl. Soft Comput. 2009, 9:226-236.
14. Tan K.S., Isa N.A.M. Color image segmentation using histogram thresholding-Fuzzy C-means hybrid approach. Pattern Recognition 2011, 44:1-15.
15. Chan C.-C.H. Intelligent spider for information retrieval to support mining-based price prediction for online auctioning. Expert Syst. Appl. 2008, 34:347-356.
16. Dhanapal R. An intelligent information retrieval agent. Knowl. Based Syst. 2008, 21:466-470.
17. Bhattacharya A., De R.K. Average correlation clustering algorithm (ACCA) for grouping of co-regulated genes with similar pattern of variation in their expression values. J. Biomed. Inf. 2010, 43:560-568.
18. Macintyre G., Bailey J., Gustafsson D., Haviv I., Kowalczyk A. Using Gene Ontology annotations in exploratory microarray clustering to understand cancer etiology. Pattern Recognition Lett. 2010, 31:2138-2146.
19. B. Zhang, M. Hsu, U. Dayal, K-harmonic means-a data clustering algorithm, Technical Report HPL-1999-124, Hewlett-Packard Laboratories, 1999.
20. B. Zhang, M. Hsu, U. Dayal, K-harmonic means, in: International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining (TSDM2000), Lyon, France, September 12, 2000.
21. Selim S.Z., Alsultan K. A simulated annealing algorithm for the clustering problem. Pattern Recognition 1991, 24:1003-1008.
22. Sung C.S., Jin H.W. A tabu-search-based heuristic for clustering. Pattern Recognition 2000, 33:849-858.
23. Bandyopadhyay S., Maulik U. An evolutionary technique based on K-means algorithm for optimal clustering in RN. Inf. Sci. 2002, 146:221-237.
24. Shelokar P.S., Jayaraman V.K., Kulkarni B.D. An ant colony approach for clustering. Anal. Chim. Acta 2004, 509:187-195.
25. Yang Y., Kamel M.S. An aggregated clustering approach using multi-ant colonies algorithms. Pattern Recognition 2006, 39:1278-1289.
26. Kao Y.-T., Zahara E., Kao I.-W. A hybridized approach to data clustering. Expert Syst. Appl. 2008, 34:1754-1762.
27. Yang F., Sun T., Zhang C. An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization. Expert Syst. Appl. 2009, 36:9847-9852.
28. Niknam T., Amiri B. An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Appl. Soft Comput. 2010, 10:183-197.
29. Banharnsakun A., Achalakul T., Sirinaovakul B. The Best-so-far selection in Artificial Bee Colony algorithm. Appl. Soft Comput. 2011, 11:2888-2901.
30. D. Karaboga, An idea based on honey bee swarm for numerical optimization, Technical Report-TR06, Engineering Faculty, Computer Engineering Department, Erciyes University, Turkey, 2005.
31. Biesmeijer J.C., Seeley T.D. The use of waggle dance information by honey bees throughout their foraging careers. Behav. Ecol. Sociobiol. 2005, 59:133-142.
32. Karaboga D., Ozturk C. A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl. Soft Comput. 2011, 11:652-657.
33. Zhang C., Ouyang D., Ning J. An Artificial Bee Colony approach for clustering. Expert Syst. Appl. 2010, 37:4761-4767.
34. Banharnsakun A., Achalakul T., Sirinaovakul B. Job shop scheduling with the Best-so-far ABC. Eng. Appl. Artif. Intell. 2011, 10.1016/j.engappai.2011.08.003.
35. Girard M.B., Mattila H.R., Seeley T.D. Recruitment-dance signals draw larger audiences when honey bee colonies have multiple patrilines. Insectes Soc. 2011, 58:77-86.
36. González A., Valero-García M., De.Cerio L. Executing algorithms with hypercube topology on torus multicomputers. IEEE Trans. Parallel Distributed Syst. 1995, 6:803-814.
37. Quinn M.J. Parallel Programming in C With MPI and OpenMP 2004, Mc Graw Hill, New York, USA.
38. Grama A., Gupta A., Karypis G., Kumar V. Introduction to Parallel Computing 2003, Addison-Wesley, New York, USA.
39. A. Banharnsakun, T. Achalakul, B. Sirinaovakul, Artificial Bee Colony algorithm on distributed environments, in: Proceedings of the World Congress on Nature and Biologically Inspired Computing (NaBIC2010), 2010, pp. 13-18.
40. Karaboga D., Basturk B. A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm. J. Global Optim. 2007, 39:459-471.
41. C.L. Blake, C.J. Merz, UCI repository of machine learning databases [Online], Available: , 1998. http://archive.ics.uci.edu/ml/datasets.html.
42. Taetragool U., Achalakul T. Method for failure pattern analysis in disk drive manufacturing. Int. J. Comput. Integrated Manuf. 2011, 24:834-846.
43. Fathian M., Amiri B. A honey-bee mating approach for cluster analysis. Int. J. Adv. Manuf. Technol. 2008, 38:809-821.
44. Niknam T., Olamaie J., Amiri B. A hybrid evolutionary algorithm based on ACO and SA for cluster analysis. J. Appl. Sci. 2008, 8:2695-2702.
45. Dalli A. Adaptation of the F-measure to Cluster-Based Lexicon Quality Evaluation 2003, EACL, Budapest.
46. Handl J., Knowles J., Dorigo M. On the performance of ant-based clustering, design and application of hybrid intelligent systems. Front. Artif. Intell. Appl. 2003, 104:204-213.