CFP last date
20 December 2024
Reseach Article

Dual Population Genetic Algorithm (GA) versus OpenMP GA for Multimodal Function Optimization

by A. J. Umbarkar, M. S. Joshi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Number 19
Year of Publication: 2013
Authors: A. J. Umbarkar, M. S. Joshi
10.5120/10744-5516

A. J. Umbarkar, M. S. Joshi . Dual Population Genetic Algorithm (GA) versus OpenMP GA for Multimodal Function Optimization. International Journal of Computer Applications. 64, 19 ( February 2013), 29-36. DOI=10.5120/10744-5516

@article{ 10.5120/10744-5516,
author = { A. J. Umbarkar, M. S. Joshi },
title = { Dual Population Genetic Algorithm (GA) versus OpenMP GA for Multimodal Function Optimization },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 64 },
number = { 19 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 29-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume64/number19/10744-5516/ },
doi = { 10.5120/10744-5516 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:17:24.982397+05:30
%A A. J. Umbarkar
%A M. S. Joshi
%T Dual Population Genetic Algorithm (GA) versus OpenMP GA for Multimodal Function Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 64
%N 19
%P 29-36
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Genetic algorithms (GAs) are useful for solving multimodal problems. It is quite difficult to search the search space of the multimodal problem with large dimensions. There is a challenge to use all the core of the system. The Dual Population GA (DPGA) attempts to explore and exploit search space on the multimodal problems. Parallel GAs (PGAs) are better option to optimize multimodal problems. OpenMP GA is parallel version of GA. The Dual Population GA (DPGA) uses an extra population called reserve population to provide additional diversity to the main population through crossbreeding. DPGA and PGA, both provide niching technique to find optimal solution. Paper presents the experimentation of DPGA, OpenMP GA and SGA. The experimentation results show that the performance of the OpenMP GA is remarkably superior to that of the SGA in terms of execution time and speed up. OpenMP GA gives optimum solution in comparison with OpenMP GA and SGA for same parameter settings.

References
  1. August A. D. , Chiou K. P. D, Sendag R. , Yi J. J. , (2010). "Programming Multicores: Do Application Programmers Need to Write Explicitly Parallel Programs?", Computer Architecture Debates in IEEE MICRO, pp. 19-32.
  2. Konfrst Z. , (2004). "Parallel Genetic Algorithm: Advances, Computing Trends, application and Perspective", In proceeding of 18th International Parallel and Distributed Processing Symposium [IPDPS'04], IEEE Computer Society.
  3. Cant´u-Paz E. , (2000). "Efficient and Accurate Parallel Genetic Algorithms", Kluwer Academic Publishers.
  4. Cantú-Paz E. , (2002). "A Survey of Parallel Genetic Algorithms", Department of Computer Science and Illinois Genetic Algorithms Laboratory University of Illinois at Urbana-Champaign.
  5. Farmer J. D. , Packard N. , Perelson A. , (1986). "The immune system, adaptation and machine learning", Physica 22 pp. 187–204.
  6. Dorigo M. , (1992). "Optimization, learning and natural algorithms", PhD Dissertation, Politecnico di Milano, Italy.
  7. Kennedy J. , Eberhart R. C. , (1995). "Particle swarm optimization", Proceedings IEEE International Conference on Neural Networks, Piscataway, pp. 1942–1948.
  8. Hykin S. S. , (1999). Neural Network: A comprehensive Foundation, Prentice hall, pp. 1-889.
  9. Storn R. , Price K. , (1997). "Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces", J. Glob. Optim. 11, pp. 341–359.
  10. Geem Z. W. , Kim J. H. , Loganathan G. V. , (2001). "A new heuristic optimization algorithm: Harmony Search Simul", the Soc. for Model and Simul. Int. 76(2), pp. 60–68.
  11. Passino K. M. , (2002). "Biomimicry of bacterial foraging for distributed optimization and control", IEEE Control Syst. Mag. 22, pp. 52–67.
  12. Eusuff E. , Lansey E. , (2003). "Optimization of water distribution network design using the shuffled frog leaping algorithm", J. Water Resour. Plan Manag. ASCE 129, pp. 210–225.
  13. Karaboga D. , (2005). "An idea based on honey bee swarm for numerical optimization", Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, Turkey.
  14. Simon D. , (2008). "Biogeography-based optimization", IEEE Trans Evol. Comput. 12, pp. 702–713.
  15. Rashedi E. , Nezamabadi-pour H. , Saryazdi S. , (2009). "GSA: a gravitational search algorithm" Inf Sci 179, pp. 2232–2248.
  16. Ahrari A. , Atai A. , (2010). "Grenade Explosion Method-A novel tool for optimization of multimodal functions", Appl Soft Comput 10(4), pp. 1132–1140.
  17. Rao R. V. , Savsani V. J. , (2012). "Mechanical Design Optimization Using Advanced Optimization Techniques", Springer Series in Advanced Manufacturing, Springer-Verlag London.
  18. Yu X. , Gen M. , (2010) Introduction to Evolutionary Algorithms, Springer-Verlag London Limited, pp. 105-111.
  19. Luque G. , Alba E. , (2011). Parallel Genetic Algorithms, Springer.
  20. Gagn´e C. , Parizeau M. , Dubreuil M. , (2003). "The Master-Slave Architecture for Evolutionary Computations Revisited", Proceedings of the Genetic and Evolutionary Computation Conference, Chicago, IL, 2, pp. 1578-1579.
  21. Lin S. , Punch W. , Goodman E. , (1994). "Coarse-grain parallel genetic algorithms: categorization and analysis", In IEEE Symposium on Parallel and Distributed Processing, pp. 27–36.
  22. Hauser R. , Männer R. , (1994). "Implementation of Standard Genetic Algorithms on MIMD Machines", In Parallel Problem Solving from Nature [PPSN3], pp. 504-513.
  23. Tanese R. , (1987). "Parallel Genetic Algorithm for a Hypercube", In Proceedings of the Second International Conference on Genetic Algorithms [ICGA2], pp. 177-183.
  24. Tanese R. , (1989). "Distributed Genetic Algorithms", In Proceedings of the Third International Conference on Genetic Algorithms [ICGA3], pp. 434-439.
  25. Voigt H. M. , Born J. , Santibanez-Koref I. , (1991) "Modeling and Simulation of Distributed Evolutionary Search Processes for Function Optimization", In Parallel Problem Solving from Nature [PPSN1], pp. 373-380.
  26. Voigt H. M. , Born J. , Santibanez-Koref I. , (1992). "Hierarchically Structured Distributed Genetic Algorithm", In Parallel Problem Solving from Nature [PPSN2], pp. 145-154.
  27. Imade H. , Morishita R. , Ono I. , Ono N. , Okamoto M. , (2003). "A grid-oriented genetic algorithm for estimating genetic networks by s-systems", SICE 2003 Annual Conference, 3(4-6), pp. 2750–2755.
  28. Herrera J. , Huedo E. , Montero R. , Llorente I. , (2005). "A gridoriented genetic algorithm", In Advances in Grid Computing - EGC 2005, pp. 315–322.
  29. Imade H. , Morishita R. , Ono I. , Ono N. , Okamoto M. , (2004). "A grid-oriented genetic algorithm framework for bioinformatics", New Gen. Comput. , 22(2), pp. 177–186.
  30. Wong M. , Wong T. , (2006). "Parallel hybrid genetic algorithms on consumer-level graphics hardware", in Congress on Evolutionary Computation, Canada, pp. 2972-2980.
  31. Arora R. , Tulshyan R. , Deb K. , (2010). "Parallelization of binary and real-coded genetic algorithm on GPU using CUDA", in Congress on Evolutionary Computation, pp. 1-8.
  32. Vidal P. Alba E. , (2010). "A multi-GPU implementation of a cellular genetic algorithm", in 2010 IEEE Congress on Evolutionary Computation.
  33. Oiso M. , Matumura Y. , (2011). "Accelerating Steady-state genetic algorithms based on CUDA architecture", , in 2011 IEEE Congress on Evolutionary Computation, pp. 687-692.
  34. Zheng L. , et. al. (2011). "Architecture-based Performance Evaluation of Genetic Algorithms on Multi/Many-core Systems", In proceeding of: 14th IEEE International Conference on Computational Science and Engineering, CSE 2011, Dalian, China, pp. 321-334.
  35. Molga M. , Smutnicki C. , (2005) "Test functions for optimization needs- 2005, unpublished.
  36. Mohan C. , Deep K. , (2009). "Optimization Techniques" first edition, New Age International Publication.
  37. Umbarkar A. J. and Joshi M. S. , (2012). "Serial DPGA vs Parallel Multithreaded DPGA: Threading Aspects", in Proceedings of the International Conference on Soft Computing for Problem Solving (SOCPROS 2011)" AISC 130, Springer pp. 37-49.
  38. Park T. , Ryu K. R. , (2007). "A dual population genetic algorithm with evolving diversity", In IEEE Congress on Evolutionary Computation (CEC2007), pp. 3516–3522.
  39. Park T. , Ryu K. R. , (2008). "Dual population genetic algorithm for Nonstationary Optimization", Proc. Genetic Evol. Comput. Conf. (GECCO'08), pp. 1025-1032, 2008.
  40. Susan L. , Graham P. B. , Kessler M. K. , McKusick, "gprof: a Call Graph Execution Profiler1", Electrical Engineering and Computer Science Department University of California, Berkeley, California.
Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm (GA) Dual Population GA (DPGA) Serial DPGA Open Multi Processing (OpenMP) Multimodal Function Non-linear optimization problems