Abstract
This paper presents a new Cooperative Evolutionary Multi-Swarm Optimization Algorithm (CEMSO-GPU) based on CUDA parallel architecture applied to solve engineering problems. The focus of this approach is: the use of the concept of master/slave swarm with a mechanism of data sharing; and, the parallelism method based on the paradigm of General Purpose Computing on Graphics Processing Units (GPGPU) with CUDA architecture, brought by NVIDIA corporation. All these improvements were made aiming to produce better solutions in fewer iterations of the algorithm and to improve the search for best results. The algorithm was tested for some well-known engineering problems (WBD, ATD, MWTCS, SRD-11) and the results compared to other approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bastos Filho, C.J.A., Caraciolo, M.P., Miranda, P.B.C., Carvalho, D.F: Multi ring PSO. In: The 10th Brazilian Symposium on Neural Networks (SBRN’2008), pp. 111–116 (2008)
Lopes, H.S., Takahashi, R.H.C.: Computação Evolucionária em Problemas de Engenharia, 1st edn. Ed. OMNIPAX, (2011) (in portuguese)
Miranda, V., Fonseca, N.: EPSO—Evolutionary particle swarm optimization, a new algorithm with applications in power systems. In: Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES, vol. 2, pp. 745–750 (2002)
Van Den Bergh, H., Engelbrecht, A.P.: A Cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8, 225–239 (2004)
Kirk, D.B., Hwu, W.M.: Programming Massively Parallel Processors a Hands-on Approach, 1st edn. Elsevier, Oxford (2010)
Solomon, S., Thulasiraman, P., Thulasiraman, R.: Collaborative multi-swarm PSO for task matching using graphics processing units. In: GECCO ’11 Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, vol. 2, pp. 1563–1570 (2011)
Mussi, L., Nashed, Y.S.G., Cagnoni, S.: GPU-based asynchronous particle swarm optimization. In: GECCO ’11 Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, vol. 2, pp. 1555–1562 (2011)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Eberhart, R., Shi, Y.: Comparing inertia weights and constriction factors. In: Proceedings of the Congress on Evolutionary Computing, pp. 84–89 (2000)
Eberhart, R., Shi, Y.: A modified particle swarm optimizer. In: IEEE International Conference of Evolutionary Computation, pp. 69–73. Anchorage, Alaska (1998)
Leite, H., Barros, J., Miranda, V.: The evolutionary algorithm EPSO to coordinate directional overcurrent relay. In: 10th IET International Conference Developments in Power System Protection (DPSP 2010) Managing the Change, pp. 1–5 (2010)
Sanders, J., Kandrot, E.: CUDA by Example: An Introduction to General-Purpose GPU Programming. Addison-Wesley Professional, New York (2010)
Niu, B., Zhu, Y., He, X.: Multi-population cooperative particle swarm optimization. In: Proceedings of the European Conference on Artificial Life, pp. 874–883 (2005)
Souza, D.L., Monteiro, G.D., Martins, T.C., Teixeira, O.N., Dmitriev, V.A.: PSO-GPU: accelerating particle swarm optimization. In: CUDA-Based Graphics Processing Units. GECCO 2011, ACM Digital Library, pp. 837–838 (2011)
Teixeira, O.N., Lobato, W.A.L.L., Yanaguibashi, H.S., Cavalcante, R.V., Silva, D.J.A., Oliveira, R.C.L.: Algoritmo Genético com Interação Social na Resolução de Problemas de Otimização Global com Restrições (in portuguese), Computação Evolucionária em Problemas de Engenharia, Ed. OMNIPAX, 1st edn. pp. 197–223, (2011) (in portuguese)
He, Q., Wang, L.: An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng. Appl. Artif. Intell. 20, 89–99 (2007)
Mezura-Montes, E.: Coello Coello, C.: Useful infeasible solutions in engineering optimization with evolutionary algorithms. In: Proceedings of the 4th Mexican International Conference on Artificial Intelligence, MICAI 2005, Lecture Notes on Artificial Intelligence No. 3789, pp. 652–662 (2005)
Hsu, Y.L., Liu, T.C.: Developing a fuzzy proportional-derivative controller optimization engine for engineering design optimization problems. Eng. Optim. 39(6), 679–700 (2007)
Golinski, J.: An adaptive optimization system applied to machine synthesis. Mech. Mach. Synth. 8(4), 419–436 (1973)
Brajevic, I., Tuba, M., Subotic, M.: Improved artificial bee colony algorithm for constrained problems. In: Proceedings of the 11th WSEAS International Conference on Neural Networks, Fuzzy Systems and Evolutionary Computing, Stevens Point, USA: WSEAS, pp. 185–190 (2010)
Cagnina, L., Esquivel, S., Coello, C.: Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica 32(3), 319–326 (2008)
Coello C., Montes, E.: Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv. Eng. Inform. 16, 193–203 (2002)
Acknowledgments
This work is supported financially by Research Support Foundation of Par(FAPESPA) and Federal University of Pará (UFPA).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Souza, D.L., Teixeira, O.N., Monteiro, D.C., de Oliveira, R.C.L. (2013). A New Cooperative Evolutionary Multi-Swarm Optimizer Algorithm Based on CUDA Architecture Applied to Engineering Optimization. In: Hatzilygeroudis, I., Palade, V. (eds) Combinations of Intelligent Methods and Applications. Smart Innovation, Systems and Technologies, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36651-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-36651-2_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36650-5
Online ISBN: 978-3-642-36651-2
eBook Packages: EngineeringEngineering (R0)