headerpos: 12198
 
 
 

Proceedings of the Estonian Academy of Sciences

ISSN 1736-7530 (electronic)   ISSN 1736-6046 (print)
Formerly: Proceedings of the Estonian Academy of Sciences, series Physics & Mathematics and  Chemistry
Published since 1952

Proceedings of the Estonian Academy of Sciences

ISSN 1736-7530 (electronic)   ISSN 1736-6046 (print)
Formerly: Proceedings of the Estonian Academy of Sciences, series Physics & Mathematics and  Chemistry
Published since 1952
Publisher
Journal Information
» Editorial Board
» Editorial Policy
» Archival Policy
» Article Publication Charges
» Copyright and Licensing Policy
Guidelines for Authors
» For Authors
» Instructions to Authors
Guidelines for Reviewers
» For Reviewers
» Review Form
List of Issues
» 2018
» 2017
» 2016
Vol. 65, Issue 4
Vol. 65, Issue 3
Vol. 65, Issue 2
Vol. 65, Issue 1
» 2015
» 2014
» 2013
» 2012
» 2011
» 2010
» 2009
» 2008
» Back Issues Phys. Math.
» Back Issues Chemistry
» Back issues (full texts)
  in Google. Phys. Math.
» Back issues (full texts)
  in Google. Chemistry
» Back issues (full texts)
  in Google Engineering
» Back issues (full texts)
  in Google Ecology
» Back issues in ETERA Füüsika, Matemaatika jt
Subscription Information
» Prices
Internet Links
Support & Contact
Publisher
» Staff
» Other journals

Modified particle swarm optimization algorithm based on gravitational field interactions; pp. 15–27

(Full article in PDF format) doi: 10.3176/proc.2016.1.01


Authors

Margarita Spichakova

Abstract

In this paper we present the modified particle swarm optimization algorithm, where gravitational interactions between particles are used for computing learning coefficients. The behaviour of the algorithm is demonstrated by solving the twodimensional Diophantine equation problem. This allows us to observe the search space and workflow of the algorithm directly on the two-dimensional plane.

Keywords

particle swarm optimization, Diophantine equation solver, gravitationally inspired heuristic search.

References

  1. Abraham , S. , Sanya , S. , and Sanglikar , M. A. Particle swarm optimization based diophantine equation solver. CoRR , abs/1003.2724 , 2010.

  2. Formato , R. A. Central force optimization: a new metaheuristic with applications in applied electromagnetics. PIER , 2007 , 77 , 425–491.
http://dx.doi.org/10.2528/PIER07082403

  3. Hsiao , Y.-T. , Chuang , C.-L. , Jiang , J.-A. , and Chien , C.-C. A novel optimization algorithm: space gravitational optimization. In Systems , Man and Cybernetics , 2005 IEEE International Conference , Vol. 3. 2005 , 2323–2328.

  4. Hsiung , S. and Mattews , J. Genetic algorithm example: Diophantine equation , 1999. www.generation5.org [accessed 23 May 2015].

  5. Kennedy , J. and Eberhart , R. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks , Vol. 4. 1995 , 1942–1948.
http://dx.doi.org/10.1109/ICNN.1995.488968

  6. Luke , S. Essentials of Metaheuristics. Lulu , second edition , 2013. Available at http://cs.gmu.edu/$\sim$sean/book/metaheuristics/ [accessed 23 May 2015].

  7. Mirjalili , S. and Hashim , S. Z. M. A new hybrid PSOGSA algorithm for function optimization. In Proceedings of International Conference on Computer and Information Application (ICCIA). 2010 , 374–377.
http://dx.doi.org/10.1109/iccia.2010.6141614

  8. Mo , S. , Zeng , J. , and Xu , W. An extended particle swarm optimization algorithm based on self-organization topology driven by fitness. J. Comput. Inform. Syst. , 2011 , 7(12) , 4441–4454.

  9. Qi , K. , Lei , W. , and Qidi , W. A novel self-organizing particle swarm optimization based on gravitation field model. In American Control Conference , 2007 , ACC '07 , 2007 , 528–533.
http://dx.doi.org/10.1109/acc.2007.4282541

10. Rashedi , E. , Nezamabadi-pour , H. , Saryazdi , S. , and Farsangi , M. M. Allocation of static var compensator using gravitational search algorithm. In First Joint Congress on Fuzzy and Intelligent Systems Ferdowsi University of Mashhad , Iran , August , 29–31. 2007 , 29–31.

11. Rashedi , E. , Nezamabadi-pour , H. , and Saryazdi , S. GSA: a gravitational search algorithm. Inform. Sci. , 2009 , 179(13) , 2232–2248.
http://dx.doi.org/10.1016/j.ins.2009.03.004

12. Rashedi , E. , Nezamabadi-pour , H. , and Saryazdi , S. Filter modeling using gravitational search algorithm. Eng. Appl. Artif. Intell. , 2011 , 24 , 117–122.
http://dx.doi.org/10.1016/j.engappai.2010.05.007

13. Tsai , H.-C. , Tyan , Y.-Y. , Wu , Y.-W. , and Lin , Y.-H. Gravitational particle swarm. Appl. Math. Comput. , 2013 , 219(17) , 9106–9117.
http://dx.doi.org/10.1016/j.amc.2013.03.098

14. Webster , B. Solving Combinatorial Optimization Problems Using a New Algorithm Based on Gravitational Attraction. PhD thesis , Florida Institute of Technology , Melbourne , FL , USA , 2004.

15. Webster , B. and Bernhard , P. J. A Local Search Optimization Algorithm Based on Natural Principles of Gravitation. Technical Report CS-2003-10 , Florida Institute of Technology , 2003.

16. Zibanezhad , B. , Zamanifar , K. , Nematbakhsh , N. , and Mardukhi , F. An approach for web services composition based on QoS and gravitational search algorithm. In Proceedings of the 6th International Conference on Innovations in Information Technology , IIT'09. IEEE Press , Piscataway , NJ , USA , 2009 , 121–125.
http://dx.doi.org/10.1109/iit.2009.5413773

 
Back

Current Issue: Vol. 67, Issue 1 in Press, 2018




Publishing schedule:
No. 1: 20 March
No. 2: 20 June
No. 3: 20 September
No. 4: 20 December