We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Hybrid Swarm Intelligence Technique for Solving Integer Multi-objective Problems

by Ibrahim M. El-henawy, Mahmoud M. Ismail
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Number 3
Year of Publication: 2014
Authors: Ibrahim M. El-henawy, Mahmoud M. Ismail
10.5120/15192-3571

Ibrahim M. El-henawy, Mahmoud M. Ismail . A Hybrid Swarm Intelligence Technique for Solving Integer Multi-objective Problems. International Journal of Computer Applications. 87, 3 ( February 2014), 45-50. DOI=10.5120/15192-3571

@article{ 10.5120/15192-3571,
author = { Ibrahim M. El-henawy, Mahmoud M. Ismail },
title = { A Hybrid Swarm Intelligence Technique for Solving Integer Multi-objective Problems },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 87 },
number = { 3 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 45-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume87/number3/15192-3571/ },
doi = { 10.5120/15192-3571 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:05:00.680232+05:30
%A Ibrahim M. El-henawy
%A Mahmoud M. Ismail
%T A Hybrid Swarm Intelligence Technique for Solving Integer Multi-objective Problems
%J International Journal of Computer Applications
%@ 0975-8887
%V 87
%N 3
%P 45-50
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The multi-objective integer programming problems are considered time consuming. In the past, mathematical structures were used that can get benefits of high processing powers and parallel processing. A general approach to generate all non-dominated solutions of the multi-objective integer programming (MOIP) Problem is developed. In this paper, a hybridization of two different swarm intelligent approaches, stochastic diffusion search, and particle swarm optimization techniques is presented for solving integer multi-objective problems. The hybrid implementation allows us to avoid certain drawbacks and weaknesses of each algorithm, which means that we are able to find an optimal solution in an acceptable computational time. Our hybrid implementation allows the MOIP algorithm to reach the optimal solution in a considerably shorter time than is needed to solve the model using the entire dataset directly within the model. Our hybrid approach outperforms the results obtained by each technique separately. It is able to find the optimal solution in a shorter time than each technique on its own, and the results are highly competitive with the state-of-the-art in large-scale optimization. Furthermore, according to our results, combining the PSO with SDS approach for solving IP problems appears to be an interesting research area in combinatorial optimization.

References
  1. Arbel, A. , and Korhonen, P. J. 2001. Using objective values to start multiple objective linear programming algorithms. European Journal of Operational Research, 128:587–596.
  2. Banzhaf, W. , Nordin, P. , Keller, R. E. , and Francone, F. D. 1998. Genetic Programming-An Introduction, Morgan Kaufmann. San Francisco. Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
  3. Bishop, J. 1989. Stochastic searching networks, London, UK, Proc. 1st IEE Conf. on Artificial Neural Networks. 329–331.
  4. Carlos, A. CoelloCoellol, and Gary, B. , Lamont, A. 2004. Applications of Multi-objective Evolutionary Algorithms. ISBN 978-981-256-106-0, 981-256-106-4.
  5. Colorni, A. , Dorigo, M. , Maniezzo, V. 1992. Distributed Optimization by Ant Colonies. In: Varela, F. , Bourgine, P. (eds. ) Proceedings of the First European Conference on Artifical Life, MIT Press, Cambridge. 134–142.
  6. David, A. , Van Veldhuizen, and Lamont, G. B. 1998. Evolutionary Computation and Convergence to a Pareto Front. In John R. Koza, editor, Late Breaking Papers at the Genetic Programming 1998 Conference, Stanford University, California. Stanford University Bookstore. 221-228.
  7. del Valle, Y. , Venayagamoorthy, G. K. , Mohaghenghi, S. , Hernandez, J. C. , Harley, R. G. 2008. Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems. IEEE Transactions on Evolutionary Computation 12 . 171–195.
  8. DeMeyer, K. , Bishop, J. M. , and Nasuto, S. J. 2003. Stochastic diffusion: Using recruitment for search. Evolvability and interaction: evolutionary substrates of communication, signalling, and perception in the dynamics of social complexity (ed. P. McOwan, K. Dautenhahn& CL Nehaniv) Technical Report. 60–65.
  9. Dorigo, M. , Maniezzo, V. , Colorni, A. 1996. The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics 26. 29–41.
  10. Eberhart, R. C. , Simpsonand, P. K. Dobbins, R. W. 1996. Computational Intelligence PC Tools, Academic Press Professional. Boston.
  11. Eckart Zitzler and Lothar Thiele. 1999. Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. Evolutionary Computation, IEEE Transactions on Volume 3, Issue 4. 257- 271.
  12. Grosan, C. , Abraham, A. , Monica, C. 2006. Swarm Intelligence in Data Mining. In: Abraham, A. , Grosan, C. , Ramos, V. (eds. ) Swarm Intelligence in Data Mining. SCI, vol. 34, Springer, Heidelberg. 1–16.
  13. Kennedy, J. and Eberhart, R. C. 2001. Swarm Intelligence, Morgan Kaufmann Publishers.
  14. Kennedy, J. , Eberhart, R. 1995. Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4. 1942–1948.
  15. Lai, T. H. , Sahni, S. 1984. Anomalies in parallel B&B algorithms, Res. Contrib. 27 (6) 594-602.
  16. Laskari, E. C. , Parsopoulos, K. C. and Vrahatis, M. N. 2002. Particle Swarm Optimization for Integer Programming, Proceedings IEEE Congress on Evolutionary Computation, Hawaii, U. S. A. 1576-1581.
  17. Mezmaz,M. , Melab, N. , and Talbi,E. G. 2007. An efficient load balancing strategy for grid-based branch and bound algorithm. Parallel Computing, volume: 33, number: 4-5, 2007, ISSN: 0167-8191. Elsevier Science Publishers B. V. , Amsterdam, The Netherlands. 302-313.
  18. Myatt, D. R. , Bishop, J. M. , and Nasuto, S. J. 2004. Minimum stable convergence criteria for stochastic diffusion search. Electronics Letters, 40(2). 112–113.
  19. Nasuto, S. J. 1999. Resource Allocation Analysis of the Stochastic Diffusion Search. PhDthesis, University of Reading, Reading, UK.
  20. Nasuto, S. J. and Bishop, J. M. 1999. Convergence analysis of stochastic diffusion search. Parallel Algorithms and Applications, 14(2).
  21. Nasuto, S. J. , Bishop, J. M. , and Lauria, S. 1998. Time complexity of stochastic diffusion search. Neural Computation, NC98.
  22. Passino, K. M. 2000. Distributed Optimization and Control Using Only a Germ of Intelligence. In: Proceedings of the 2000 IEEE International Symposium on Intelligent Control. 5–13.
  23. Passino, K. M. 2002 Biomimicry of Bacteria Foraging for Distributed Optimization and Control. IEEE Control Systems Magazine 22. 52–67.
  24. Santana-Quintero, L. V. and Coello, C. A. 2005. an Algorithm Based on Differential Evolution for Multi-Objective Problems, International Journal of Computational Intelligence Research, ISSN 0973-1873 Vol. 1, No. 2. 151–169.
  25. Seeley, T. D. 1996. The Wisdom of the Hive. Harward University Press.
  26. Teodorovic, D. , Dell'orco, M. 2005. Bee Colony Optimization-A Cooperative Learning Approach to Complex Transportation Problems, Advanced OR and AI Methods in Transportation. 51–60.
  27. Yu, B. , Yuan, X. and Wang, J. 2000. Short-Term Hydro-Thermal Scheduling using Particle Swarm Optimization Method. Energy Conversion and Management, Vol. 48. 1902-1908.
Index Terms

Computer Science
Information Sciences

Keywords

Swarm Intelligence Integer programming Multi-objective Stochastic Diffusion Search and Particle Swarm Optimization

Powered by PhDFocusTM