Towards the novel reasoning among particles in PSO by the use of RDF and SPARQL

The Scientific World Journal

Volumn 2014, Issue , 2014, Pages

  Fister, Iztok ^a   Yang, Xin She ^b   Ljubič, Karin ^a   Fister, Dušan ^a   Brest, Janez ^a  

^a UNIVERSITY OF MARIBOR   (Slovenia)

^b MIDDLESEX UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; INTERNET; NEIGHBORHOOD; PARTICLE SWARM OPTIMIZATION; POPULATION DISTRIBUTION; POPULATION MODEL; RESOURCE DEFINITION FRAMEWORK; RESOURCE DEFINITION FRAMEWORK QUERY LANGUAGE; SEMANTICS; THEORETICAL MODEL;

ALGORITHMS; INTERNET; MODELS, THEORETICAL;

EID: 84899433810     PISSN: None     EISSN: 1537744X     Source Type: Journal    
DOI: 10.1155/2014/121782     Document Type: Article

References (35)

1. Wolpert D. H., Macready W. G., No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1997 1 1 67 82 2-s2.0-0031118203 10.1109/4235.585893
2. Russell S., Norvig P., Artificial Intelligence: A Modern Approach 2009 New York, NY, USA Prentice Hall
3. Eiben A. E., Smith J. E., Introduction to Evolutionary Computing 2003 Berlin, Germany Springer
4. Blum C., Merkle D., Swarm Intelligence: Introduction and Applications 2008 Berlin, Germany Springer
5. Darwin C., The Origin of Species 1859 London, UK John Murray
6. Paszkowicz W., Genetic algorithms, a nature-inspired tool: survey of applications in materials science and related fields. Materials and Manufacturing Processes 2009 24 2 174 197 2-s2.0-61549123642 10.1080/10426910802612270
7. Goldberg D., Genetic Algorithms in Search, Optimization, and Machine Learning 1996 Massachusetts, Mass, USA Addison-Wesley
8. Koza J., Genetic Programming 2-Automatic Discovery of Reusable Programs 1994 Cambridge, Mass, USA The MIT Press
9. Bck T., Evolutionary Algorithms in Theory and Practice-Evolution Strategies, Evolutionary Programming, Genetic Algorithms 1996 Oxford, UK University Press
10. Fogel L., Owens A., Walsh M., Artificial Intelligence through Simulated Evolution 1996 New York, NY, USA John Wiley & Sons
11. Storn R., Price K., Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 1997 11 4 341 359 2-s2.0-0142000477
12. Brest J., Greiner S., Bošković B., Mernik M., Žumer V., Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation 2006 10 6 646 657 2-s2.0-33847199831 10.1109/TEVC.2006.872133
13. Das S., Suganthan P. N., Differential evolution: a survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation 2011 15 1 4 31 2-s2.0-79952003251 10.1109/TEVC.2010.2059031
14. Kennedy J., Eberhart R., Particle swarm optimization. 4 Proceedings of the IEEE International Conference on Neural Networks December 1995 Perth, Australia IEEE 1942 1948 2-s2.0-0029535737 10.1109/ICNN.1995.488968
15. Fister I., Fister I. Jr., Yang X. -S., Brest J., A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation 2013 13 34 46 10.1016/j.swevo.2013.06.001
16. Fister I., Yang X. -S., Brest J., Fister I. Jr., Modified firefly algorithm using quaternion representation. Expert Systems with Applications 2013 40 18 7220 7230 10.1016/j.eswa.2013.06.070
17. Fister Jr I., Fister D., Fister I., A comprehensive review of cuckoo search: variants and hybrids. International Journal of Mathematical Modelling and Numerical Optimisation 2013 4 4 387 409 10.1504/IJMMNO.2013.059205
18. Yang X. -S., A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) 2010 New York, NY, USA Springer 65 74
19. Fister Jr I., Fister D., Fister I., Differential evolution strategies with random forest regression in the bat algorithm. Proceeding of the 15th Annual Conference Companion on Genetic and Evolutionary Computation 2013 ACM 1703 1706
20. Suganthan P. N., Particle swarm optimiser with neighbourhood operator. 3 Proceedings of the Congress on Evolutionary Computation (CEC '99) July 1999 Washington, Wash, USA IEEE 10.1109/CEC.1999.785514
21. Liu H., Abraham A., Choi O., Moon S. H., Variable neighborhood particle swarm optimization for multi-objective flexible job-shop scheduling problems. Simulated Evolution and Learning 2006 4247 New York, NY, USA Springer 197 204 Lecture Notes in Computer Science 10.1007/11903697-26
22. Kennedy J., Particle swarm optimization. Encyclopedia of Machine Learning 2010 New York, NY, USA Springer 760 766
23. Chakraborti N., Jayakanth R., Das S., Çalişir E. D., Erkoç Ş., Evolutionary and genetic algorithms applied to Li+-C system: calculations using differential evolution and particle swarm algorithm. Journal of Phase Equilibria and Diffusion 2007 28 2 140 149 2-s2.0-34249845748 10.1007/s11669-007-9019-8
24. Eberhart R. C., Shi Y., Particle swarm optimization: developments, applications and resources. 1 Proceedings of the Congress on Evolutionary Computation May 2001 IEEE 81 86 2-s2.0-0034863568 10.1109/CEC.2001.934374
25. Shi Y., Eberhart R., Modified particle swarm optimizer. Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC '98) May 1998 IEEE 69 73 2-s2.0-0031700696
26. Miller E., An introduction to the resource description framework. D-Lib Magazine 1998 4 5 14 25 2-s2.0-0003267015
27. Allemang D., Hendler J., Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL 2011 2nd Amsterdam, The Netherlands Morgan Kaufmann
28. Harris S., Seaborne A., Sparql 1.1 Query Language 2013
29. rdflib: A python library for working with RDF, 2013, http://code.google. com/p/rdflib/
30. Numpy,. 2013, http://www.numpy.org/
31. Matplotlib,. 2013, http://matplotlib.org/
32. Yang X. -S., Yang X. -S., Appendix A: test problems in optimization. Engineering Optimization 2010 Hoboken, NJ, USA John Wiley & Sons 261 266
33. Friedman M., The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association 1937 32 200 675 701 10.2307/2279372
34. Friedman M., A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics 1940 11 1 86 92 10.1214/aoms/1177731944
35. Demšar J., Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 2006 7 1 30 2-s2.0-29644438050