The week's pick
Reseach Article
Eurygaster Algorithm: A New Approach to Optimization
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 57 - Number 2 |
| Year of Publication: 2012 |
| Authors: Fariborz Ahmadi, Hamid Salehi, Khosro Karimi |
10.5120/9084-2611
|
Fariborz Ahmadi, Hamid Salehi, Khosro Karimi . Eurygaster Algorithm: A New Approach to Optimization. International Journal of Computer Applications. 57, 2 ( November 2012), 9-13. DOI=10.5120/9084-2611
Abstract
Almost all of the approach to solve NP-hard and NP-complete problem simulate artificial life. In this research, the behavior of eurygaster life is studied, so according to their life the new algorithm is introduced. In spite of PSO algorithm, that is used to solve continuous nonlinear functions, researchers' algorithm is so suitable to solve both continuous and discrete functions. Eurygasters attack to grain farms and distributed over them. It is worth to mention that these insects attack to farms in groups furthermore each group colonizes in one farm. It is observed that after periods of time all of the farms in a region are occupied by these groups of eurygasters. When each group of these insects are going to seek a farm to feed on it, they consider nearly all the farms and settles on a farm which have a lowest distance with them and doesn't have any group of eurygasters. It is clear that by distributing several groups of eurygasters, depending on the problem size, on search space of problem, the solution of the problem can be extracted. In this research, using the behavior of eurygasters, a new algorithm has been invented and has been tested on graph partitioning. The evaluation results show the advantage of researcher algorithm over ancient ones like genetic and PSO.
References
- R. C. Eberhart, and J. Kennedy, "A new optimizer using particle swarm theory". In Proceedings of the sixth international symposium on micro machine and human science, volume 43. New York, NY, USA: IEEE, 1995.
- J. Kennedy, and R. C. Eberhart, "Particle swarm optimization". In Proceedings of IEEE international conference on neural networks, volume 4, pages 1942–1948. Perth, Australia, 1995.
- M. Dorigo, "Optimization, Learning and Natural Algorithms", PhD thesis, Politecnico di Milano, Italie, 1992.
- S. Kirkpatrick, C. D. JR. Gelatt, and M. P. Vecchi, "Optimization by simulated annealing", IBM Research Report RC 9355.
- F. Glover, and M. Laguna, "Tabu Search", Kluwer Academic Publishers, Boston, 1997.
- Pham, DT. Ghanbarzadeh, A. Koc, E. Otri, S. Rahim, and M. Zaidi, "The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.
- H. Murase, "Finite element analysis using a photosynthetic algorithm", Computers and Electronics in Agriculture, 29, pp. 115-123, 2000.
- X. S. Yang, " New enzyme algorithm", Tikhonov regulation and inverse parabolic analysis, in Advances in Computational Methods in Science and Engineering, Lecture Series on Computer and Computer Sciences, ICCMSE, Eds. T. Simons and G. Maroulis, 4, 1880-1883, 2005.
- K. N. Krishnanand, and D. Ghose, "Detection of multiple source locations using a glowworm metaphor with applications to collective robotics," IEEE Swarm Intelligence Symposium, Pasadena, California, USA, pp. 84–91, 2005.
- A. Mucherino, and O. Seref, "Monkey Search: A Novel Meta-Heuristic Search for Global Optimization,"AIP Conference Proceedings 953, Data Mining, System Analysis and Optimization in Biomedicine, 162–173, 2007.
- X. S. Yang, "Firefly algorithm," (chapter8) in: Nature-inspired Metaheuristic Algorithms, Luniver Press, 2008.
- D. Doval, S. Mancoridis, and B. Mitchell,"Automatic clustering of software systems using a genetic algorithm," STEP '99, IEEE Computer Society, 1999.
- Y. Shi, and R. Eberhart, "A Modified Particle Swarm Optimizer," IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, May 4-9, 1998.
- P. Mathiyalagan, U. R. Dhepthie, and S. N. Sivanandam, "Grid Scheduling Using Enhanced PSO Algorithm," (IJCSE) International Journal on Computer Science and Engineering, Vol. 02, No. 02, 140-145, 2010.
- R. J. Collins, and D. R. Jefferson, "The evolution of sexual selection and female choice," In F. J. Varela and P. Bourgine, eds. , Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life. MIT Press, 1992.
- D. Ackley, and M. Littman, "Interactions between learning and evolution," In C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, eds. , Artificial Life II. Addison?Wesley, 1992.
- L. Altenberg, "The evolution of evolvability in genetic programming," In K. E. Kinnear, Jr. , ed. ,Advances in Genetic Programming. MIT Press, 1994.
- R. M. French, and A. Messinger, "Genes, phenes, and the Baldwin effect: Learning and evolution in a simulated population," In R. A. Brooks P. Maes, eds. , Artificial Life IV. MIT Press, 1994.
Index Terms
Keywords