Soft Computing

, Volume 15, Issue 11, pp 2221–2232 | Cite as

Restart particle swarm optimization with velocity modulation: a scalability test

Focus

Abstract

Large scale continuous optimization problems are more relevant in current benchmarks since they are more representative of real-world problems (bioinformatics, data mining, etc.). Unfortunately, the performance of most of the available optimization algorithms deteriorates rapidly as the dimensionality of the search space increases. In particular, particle swarm optimization is a very simple and effective method for continuous optimization. Nevertheless, this algorithm usually suffers from unsuccessful performance on large dimension problems. In this work, we incorporate two new mechanisms into the particle swarm optimization with the aim of enhancing its scalability. First, a velocity modulation method is applied in the movement of particles in order to guide them within the region of interest. Second, a restarting mechanism avoids the early convergence and redirects the particles to promising areas in the search space. Experiments are carried out within the scope of this Special Issue to test scalability. The results obtained show that our proposal is scalable in all functions of the benchmark used, as well as numerically very competitive with regards to other compared optimizers.

Keywords

Continuous optimization Scalability Particle swarm optimization  Large scale benchmarking 

References

  1. Alba E, Luque G, García-Nieto J, Ordonez G, Leguizamón G. (2007) Mallba: a software library to design efficient optimisation algorithms. Int J Innov Comput Appl 2007 (IJICA) 1(1):74–85CrossRefGoogle Scholar
  2. Auger A, Hansen N (2005) A restart cma evolution strategy with increasing population size. IEEE Congr Evol Comput 2:1769–1776CrossRefGoogle Scholar
  3. Clerc M, Kennedy J (2003) The particle swarm: explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73Google Scholar
  4. Das S, Abraham A, Konar A (2008) Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspectives. Springer, BerlinGoogle Scholar
  5. Eshelman LJ (1991) The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination. In: Rawlins G, Kaufmann M (eds) Proceedings of foundations of genetic algorithms Conference, vol 1, pp 265–283Google Scholar
  6. García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the ea’s behaviour: a case study on the CEC’2005 special session on real parameter optimization, vol 15, pp 617–644Google Scholar
  7. García-Nieto J, Apolloni J, Alba E, Leguizamón G (2009) Algoritmo basado en cúmulos de partículas y evolución diferencial para la resolución de problemas de optimización continua. In: MAEB 2009 (VI Congreso español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados), Málaga, p 433–440Google Scholar
  8. Ghosh S, Kundu D, Suresh K, Das S, Abraham A, Panigrahi BK, Snasel V (2009) On some properties of the lbest topology in particle swarm optimization. In: Hybrid intelligent systems, 2009. HIS ’09. Ninth international conference, vol 3, pp 370–375, 12–14Google Scholar
  9. Hansen N, Auger A, Finck S, Ros R (2009) BBOB’09: Real-parameter black-box optimization benchmarking. Technical report RR-6828, INRIA.Google Scholar
  10. Hansen N, Auger A, Finck S, Ros R (2010) BBOB’10: Real-parameter black-box optimization benchmarking. Technical report RR-7215, INRIA.Google Scholar
  11. Hansen N, Müller SD, Petros K (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput 11(1):1–18CrossRefGoogle Scholar
  12. Herrera F, Lozano M, Molina D (2010) Components and parameters of de, real-coded chc, and g-cmaes. Technical report, SCI2S, University of Granada, SpainGoogle Scholar
  13. Herrera F, Lozano M, Molina D (2010) Test suite for the special issue of soft computing on scalability of evolutionary algorithms and other metaheuristics for large scale continuous optimization problems. Technical report, SCI2S. University of Granada, SpainGoogle Scholar
  14. Herrera F, Lozano M (2009) ISDA’09 workshop on evolutionary algorithms and other metaheuristics for continuous optimization problems—a scalability test. Technical report. University of Granada, Pisa, ItalyGoogle Scholar
  15. Hsieh S, Sun T, Liu C, Tsai S (2008) Solving large scale global optimization using improved particle swarm optimizer. Evolutionary Computation, 2008. CEC 2008. IEEE world congress on computational intelligence. IEEE congress, pp 1777–1784Google Scholar
  16. Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufmann, San FranciscoGoogle Scholar
  17. Knight JN, Lunacek M (2007) Reducing the space–time complexity of the CMA-ES. In: GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, New York, pp 658–665Google Scholar
  18. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. Evol Comput IEEE Trans 10(3):281–295CrossRefGoogle Scholar
  19. Liang JJ, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer with local search. IEEE Congr Evol Comput 1:522–528CrossRefGoogle Scholar
  20. Montes de Oca MA, Stützle T, Birattari M, Dorigo M (2009) Frankenstein’s pso: a composite particle swarm optimization algorithm. Trans Evol Comput 13(5):1120–1132CrossRefGoogle Scholar
  21. Price KV, Storn R, Lampinen J (2005) Differential evolution: a practical approach to global optimization. Springer, LondonMATHGoogle Scholar
  22. Shang Y, Qiu Y (2006) A note on the extended rosenbrock function. Evol Comput 14(1):119–126CrossRefGoogle Scholar
  23. Sheskin DJ (2003) Handbook of parametric and nonparametric statistical procedures. Chapman & Hall/CRC Statistics, London/Boca RatonCrossRefGoogle Scholar
  24. Shi Y, Eberhart RC (1998) Parameter selection in particle swarm optimization. In: EP ’98: Proceedings of the 7th international conference on evolutionary programming VII, Springer, London, pp 591–600Google Scholar
  25. Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y-P, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC’05 special session on real-parameter optimization. Technical report KanGAL report 2005005, Nanyang Technological University, Singapore and Kanpur, IndiaGoogle Scholar
  26. Suresh K, Ghosh S, Kundu D, Sen A, Das S, Abraham A (2008) Inertia-adaptive particle swarm optimizer for improved global search. In: Proceedings of ISDA ’08, IEEE Computer Society, Washington, DC, pp 253–258Google Scholar
  27. Tang K, Xiaodong Li, Suganthan PN, Yang Z, Weise T (2010) Benchmark functions for the CEC’2010 special session and competition on large scale global optimization. Technical report Nature Inspired Computation and Applications Laboratory, USTC, Nanyang Technological University, anyang Technological University, ChinaGoogle Scholar
  28. Tang K, Yao X, Suganthan PN, MacNish C, Chen YP, Chen CM, Yang Z (2007) Benchmark functions for the CEC’08 special session and competition on large scale global optimization. Technical report, Nature Inspired Computation and Applications Laboratory, USTC, ChinaGoogle Scholar
  29. Thain D, Tannenbaum T, Livny M (2005) Distributed computing in practice: the condor experience. Concurr Pract Exp 17(2–4):323–356CrossRefGoogle Scholar
  30. Tseng L, Chen, C (2008) Multiple trajectory search for large scale global optimization. Evolutionary computation, 2008. CEC 2008, IEEE world congress on computational intelligence. IEEE Congress, pp 3052–3059Google Scholar
  31. van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. Evol Comput IEEE Trans 8(3):225–239CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Departamento Lenguajes y Ciencias de la ComputaciónUniversity of MálagaMálagaSpain

Personalised recommendations