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Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm

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Abstract

This paper presents a study of the performance of TRIBES, an adaptive particle swarm optimization algorithm. Particle Swarm Optimization (PSO) is a biologically-inspired optimization method. Recently, researchers have used it effectively in solving various optimization problems. However, like most optimization heuristics, PSO suffers from the drawback of being greatly influenced by the selection of its parameter values. Thus, the common belief is that the performance of a PSO algorithm is directly related to the tuning of such parameters. Usually, such tuning is a lengthy, time consuming and delicate process. A new adaptive PSO algorithm called TRIBES avoids manual tuning by defining adaptation rules which aim at automatically changing the particles’ behaviors as well as the topology of the swarm. In TRIBES, the topology is changed according to the swarm behavior and the strategies of displacement are chosen according to the performances of the particles. A comparative study carried out on a large set of benchmark functions shows that the performance of TRIBES is quite competitive compared to most other similar PSO algorithms that need manual tuning of parameters. The performance evaluation of TRIBES follows the testing procedure introduced during the 2005 IEEE Conference on Evolutionary Computation. The main objective of the present paper is to perform a global study of the behavior of TRIBES under several conditions, in order to determine strengths and drawbacks of this adaptive algorithm.

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References

  • Adenso-Diaz, B., & Laguna, M. (2006). Fine-tuning of algorithms using fractional experimental design and local search. Operations Research, 54(1), 99–114.

    Article  Google Scholar 

  • Auger, A., Kern, S., & Hansen, N. (2005a). A restart CMA evolution strategy with increasing population size. In Proceedings of 2005 congress on evolutionary computation (pp. 1769–1776). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Auger, A., Kern, S., & Hansen, N. (2005b). Performance evaluation of an advanced local search evolutionary algorithm. In Proceedings of 2005 congress on evolutionary computation (pp. 1777–1784). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Ballester, P. J. (2005). Real-parameter optimization performance study on the CEC’2005 benchmark with SPC-PNX. In Proceedings of 2005 congress on evolutionary computation (pp. 498–505). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Bartz-Beielstein, T., Limbourg, P., Mehnen, J., Schmitt, K., Parsopoulos, K. E., & Vrahatis, M. N. (2003). Particle swarm optimizers for Pareto optimization with enhanced archiving techniques. In Proceedings of 2003 congress on evolutionary computation (pp. 1780–1787). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Battiti, R. (1996). Reactive search: toward self tuning heuristics. In Modern heuristic search methods (pp. 61–83). Hoboken: Wiley.

    Google Scholar 

  • Birattari, M., Stützle, T., Paquete, L., & Varrentrapp, K. (2002). A racing algorithm for configuring metaheuristics. In GECCO 2002: proceedings of the genetic and evolutionary computation conference (pp. 11–18). San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Clerc, M. (2005). Binary particle swarm optimisers: toolbox, derivations, and mathematical insights. https://hal.archives-ouvertes.fr/hal-00122809.

  • Clerc, M. (2006). Particle swarm optimization. In International scientific and technical encyclopaedia. Hoboken: Wiley.

    Google Scholar 

  • Clerc, M., & Kennedy, J. (2002). The particle swarm: explosion, stability, and convergence in multi-dimensional complex space. IEEE Transactions on Evolutionary Computation, 6, 58–73.

    Article  Google Scholar 

  • Di Caro, G., & Dorigo, M. (1998). AntNet: distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research (JAIR), 9, 317–365.

    MATH  Google Scholar 

  • Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 26(1), 29–41.

    Article  Google Scholar 

  • Eberhart, R. C., & Shi, Y. V. W. (1998). Parameter selection in particle swarm optimization. In LNCS : Vol. 1447. Proceedings of the 7th international conference on evolutionary programming VII (pp. 591–600). Berlin: Springer.

    Google Scholar 

  • Förster, M., Bickel, B., Hardung, B., & Kókai, G. (2007). Self-adaptive ant colony optimization applied to function allocation in vehicle networks. In Proceedings of the 9th annual conference on genetic and evolutionary computation (pp. 1991–1998). New York: ACM Press.

    Chapter  Google Scholar 

  • García-Martínez, C., & Lozano, M. (2005). Hybrid real-coded genetic algorithms with female and male differentiation. In Proceedings of 2005 congress on evolutionary computation (pp. 896–903). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Harary, F. (1994). Graph theory. Reading: Addison-Wesley.

    Google Scholar 

  • Holland, J. H. (1992). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.

    Google Scholar 

  • Hu, X., & Eberhart, R. C. (2002). Adaptive particle swarm optimization: detection and response to dynamic systems. In Proceedings of 2002 congress on evolutionary computation (pp. 1666–1670). Washington: IEEE Computer Society.

    Google Scholar 

  • Hutter, F., Hamadi, Y., Hoos, H. H., & Leyton-Brown, K. (2006). Performance prediction and automated tuning of randomized and parametric algorithms. In LNCS : Vol. 4204. Proceedings of the principles and practice of constraint programming conference (pp. 213–228). Berlin: Springer.

    Chapter  Google Scholar 

  • Ingber, L. (1996). Adaptive simulated annealing (ASA): lessons learned. Control and Cybernetics, 25(1), 33–54.

    MATH  Google Scholar 

  • Kennedy, J. (1999). Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In Proceedings of 1999 congress on evolutionary computation (pp. 1931–1938). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (pp. 1942–1948). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm intelligence. San Francisco: Morgan Kaufmann Academic Press.

    Google Scholar 

  • Kennedy, J. (2003). Bare bones particle swarms. In Proceedings of the IEEE swarm intelligence symposium (pp. 80–87). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Larrañaga, P., & Lozano, J. A. (2001). Estimation of distribution algorithms, a new tool for evolutionary computation. Norwell: Kluwer Academic.

    Google Scholar 

  • Liang, J. J., & Suganthan, P. N. (2005). Dynamic multi-swarm particle swarm optimizer with local search. In Proceedings of 2005 congress on evolutionary computation (pp. 522–528). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Liang, J. J., Suganthan, P. N., & Deb, K. (2005). Novel composition test functions for numerical global optimization. In Proceedings of the 2005 swarm intelligence symposium (pp. 68–75). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Molina, D., Herrera, F., & Lozano, M. (2005). Adaptive local search parameters for real-coded memetic algorithms. In Proceedings of 2005 congress on evolutionary computation (pp. 888–895). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Murata, Y., Shibata, N., Yasumoto, K., & Ito, M. (2002). Agent oriented self adaptive genetic algorithm. In Proceedings of the IASTED communications and computer networks (pp. 348–353). Calgary: Acta Press.

    Google Scholar 

  • Nawrocki, M., Dohler, M., & Aghvami, A. H. (2006). Understanding UMTS radio network modelling: theory and practice. Hoboken: Wiley.

    Book  Google Scholar 

  • Nakib, A., Cooren, Y., Oulhadj, H., & Siarry, P. (2007). Magnetic resonance image segmentation based on two-dimensional exponential entropy and a parameter free PSO. In LNCS : Vol. 4926. 8th international conference on artificial evolution (pp. 50–61). Berlin: Springer.

    Chapter  Google Scholar 

  • Niu, B., Zhu, Y., He, X., & Henry, W. (2005). MCPSO: a multi-swarm cooperative particle swarm optimizer. Applied Mathematics and Computation, 185(2), 1050–1062.

    Article  Google Scholar 

  • Onwubolu, G. C., & Babu, B. V. (2004). TRIBES application to the flow shop scheduling problem. In New optimization techniques in engineering (pp. 517–536). Berlin: Springer.

    Google Scholar 

  • Parsopoulos, K. E., Tasoulis, D. K., & Vrahatis, M. N. (2004). Multiobjective optimization using parallel vector evaluated particle swarm optimization. In Proceedings of the IASTED international conference on artificial intelligence and applications (pp. 823–828). Calgary: Acta Press.

    Google Scholar 

  • Particle Swarm Central. (2006). http://www.particleswarm.info/Standard_PSO_2006.c.

  • Peer, E. S., van den Bergh, F., & Engelbrecht, A. P. (2003). Using neighborhoods with the guaranteed convergence PSO. In Proceedings of the IEEE swarm intelligence symposium 2003 (SIS 2003) (pp. 235–242). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. An overview. Swarm Intelligence, 1(1), 33–57.

    Article  Google Scholar 

  • Posik, P. (2005). Real parameter optimization using mutation step co-evolution. In Proceedings of 2005 congress on evolutionary computation (pp. 872–879). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Qin, A. K., & Suganthan, P. N. (2005). Self-adaptive differential evolution algorithm for numerical optimization. In Proceedings of 2005 congress on evolutionary computation (pp. 1785–1791). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Reynolds, C. W. (1987). Flocks, herds and schools: a distributed behavioral model. Computer Graphics, 21(4), 25–34.

    Article  MathSciNet  Google Scholar 

  • Rönkkönen, J., Kukkonen, S., & Price, K. V. (2005). Real-parameter optimization with differential evolution. In Proceedings of 2005 congress on evolutionary computation (pp. 506–513). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Sawai, H., & Adachi, S. (1999). Genetic algorithm inspired by gene duplication. In Proceedings of the 1999 congress on evolutionary computation (pp. 480–487). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Schnecke, V., & Vornberger, O. (1996). An adaptive parallel genetic algorithm for VLSI-layout optimization. In LNCS : Vol. 1141. Proceedings of the 4th international conference on parallel problem solving from nature (pp. 859–868). Berlin: Springer.

    Chapter  Google Scholar 

  • Serra, P., Stanton, A. F., & Kais, S. (1997). Method for global optimization. Physical Review, 55, 1162–1165.

    Article  Google Scholar 

  • Shi, Y., & Eberhart, R. (1998). Parameter selection in particle swarm optimization. In LNCS : Vol. 1447. Proceedings of the seventh annual conference on evolutionary programming (pp. 591–600). Berlin: Springer.

    Google Scholar 

  • Shi, Y., & Eberhart, R. C. (2001). Fuzzy adaptive particle swarm optimization. In Proceedings of 2001 congress on evolutionary computation (pp. 101–106). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Sinha, A., Tiwari, S., & Deb, K. (2005). A population-based, steady-state procedure for real-parameter optimization. In Proceedings of 2005 congress on evolutionary computation (pp. 514–521). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Suganthan, P. N. (1999). Particle swarm optimization with a neighborhood operator. In Proceedings of 1999 congress on evolutionary computation (pp. 1958–1962). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Suganthan, P. N., Hansen, N., Liang, J. J., Deb, K., Chen, Y. P., Auger, A., & Tiwari, S. (2005). Problem definitions and evaluation criteria for the CEC’2005 special session on real-parameter optimization (Technical Report). Nanyang Technological University, Singapore, May 2005. http://www.ntu.edu.sg/home/epnsugan/index_files/CEC-05/Tech-Report-May-30-05.pdf.

  • Tanagra data-mining software. (2008). http://eric.univ-lyon2.fr/~ricco/tanagra/en/tanagra.html.

  • Trelea, I. C. (2003). The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 85, 317–325.

    Article  MATH  MathSciNet  Google Scholar 

  • van den Bergh, F. (2002). An analysis of particle swarm optimizers. PhD thesis, University of Pretoria, South Africa.

  • Wilson, E. O. (1975). Sociobiology: the new synthesis. Cambridge: Belknap Press.

    Google Scholar 

  • Yasuda, K., & Iwasaki, N. (2004). Adaptive particle swarm optimization using velocity information of swarm. In Proceedings of the IEEE conference on system, man and cybernetics (pp. 3475–3481). Piscataway: IEEE Press.

    Google Scholar 

  • Ye, X. F., Zhang, W. J., & Yang, Z. L. (2002). Adaptive particle swarm optimization on individual level. In Proceedings of the international conference on signal processing (ICSP) (pp. 1215–1218). Piscataway: IEEE Press.

    Google Scholar 

  • Yuan, B., & Gallagher, M. (2005). Experimental results for the special session on real-parameter optimization at CEC’2005: a simple, continuous EDA. In Proceedings of 2005 congress on evolutionary computation (pp. 1792–1799). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Zhang, W., Liu, Y., & Clerc, M. (2003). An adaptive PSO algorithm for real power optimization. In Proceedings of the APSCOM (advances in power system control operation and management) conference, S6: application of artificial intelligence technique (part I) (pp. 302–307). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Zheng, Y., Ma, L., Zhang, L., & Qian, J. (2003). On the convergence analysis and parameter selection in particle swarm optimization. In Proceedings of international conference on machine learning and cybernetics (pp. 1802–1807). Piscataway: IEEE Press.

    Google Scholar 

  • Zhong, W. C., Liu, J., Xue, Z., & Jiao, L. C. (2004). A multiagent genetic algorithm for global numerical optimization. IEEE Transactions on Systems, Man and Cybernetics, 34, 1128–1141.

    Article  Google Scholar 

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  1. Laboratoire Images, Signaux et Systèmes Intelligents, LiSSi, E.A. 3956, Université de Paris 12, 61 avenue du Général de Gaulle, 94010, Créteil, France

    Yann Cooren, Maurice Clerc & Patrick Siarry

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  1. Yann Cooren
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Correspondence to Patrick Siarry.

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Cooren, Y., Clerc, M. & Siarry, P. Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm. Swarm Intell 3, 149–178 (2009). https://doi.org/10.1007/s11721-009-0026-8

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