HYBRID RULES OF PERTURBATION IN DIFFERENTIAL EVOLUTION FOR DYNAMIC OPTIMIZATION

Mikołaj Raciborski, Krzysztof Trojanowski, Piotr Kaczyński

Abstract

This paper studies properties of a differential evolution approach (DE) for dynamic optimization problems. An adaptive version of DE, namely the jDE algorithm has been applied to two well known benchmarks: Generalized Dynamic Benchmark Generator (GDBG) and Moving Peaks Benchmark (MPB) reimplemented in a new benchmark suite Syringa. The main novelty of the presented research concerns application of new type of solution, that is, solution mutated with an operator originated from another metaheuristics. The operator uses a symmetric a-stable distribution variate for modification of the solution coordinates.

References

  1. Branke, J. (1999). Memory enhanced evolutionary algorithm for changing optimization problems. In Proc. of the Congr. on Evolutionary Computation, volume 3, pages 1875-1882. IEEE Press, Piscataway, NJ.
  2. Brest, J., Greiner, S., Boskovic, B., Mernik, M., and Zumer, V. (2006). Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput., 10(6):646-657.
  3. Brest, J., Zamuda, A., Boskovic, B., Maucec, M. S., and Zumer, V. (2009). Dynamic optimization using selfadaptive differential evolution. In IEEE Congr. on Evolutionary Computation, pages 415-422. IEEE.
  4. Feokistov, V. (2006). Differential Evolution, In Search of Solutions, volume 5 of Optimization and Its Applications. Springer.
  5. Li, C. and Yang, S. (2008). A generalized approach to construct benchmark problems for dynamic optimization. In Simulated Evolution and Learning, SEAL, volume 5361 of LNCS, pages 391-400. Springer.
  6. Liang, J. J., Suganthan, P. N., and Deb, K. (2005). Novel composition test functions for numerical global optimization. In IEEE Swarm Intelligence Symposium, pages 68-75, Pasadena, CA, USA.
  7. Price, K. V. (1994). Genetic annealing. Dr. Dobb's Journal, pages 127-132.
  8. Price, K. V., Storn, R. M., and Lampinen, J. A. (2005). Differential Evolution, A Practical Approach to Global Optimization. Natural Computing Series. Springer.
  9. Salomon, R. (1996). Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions. BioSystems, 39(3):263-278.
  10. Suganthan, P. N., Hansen, N., Liang, J. J., Deb, K., Chen, Y.-P., Auger, A., and Tiwari, S. (2005). Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization. Technical report, Nanyang Technological University, Singapore.
  11. Trojanowski, K. (2009). Properties of quantum particles in multi-swarms for dynamic optimization. Fundamenta Informaticae, 95(2-3):349-380.
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Paper Citation


in Harvard Style

Raciborski M., Trojanowski K. and Kaczyński P. (2011). HYBRID RULES OF PERTURBATION IN DIFFERENTIAL EVOLUTION FOR DYNAMIC OPTIMIZATION . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011) ISBN 978-989-8425-83-6, pages 32-41. DOI: 10.5220/0003671800320041


in Bibtex Style

@conference{ecta11,
author={Mikołaj Raciborski and Krzysztof Trojanowski and Piotr Kaczyński},
title={HYBRID RULES OF PERTURBATION IN DIFFERENTIAL EVOLUTION FOR DYNAMIC OPTIMIZATION},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011)},
year={2011},
pages={32-41},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003671800320041},
isbn={978-989-8425-83-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011)
TI - HYBRID RULES OF PERTURBATION IN DIFFERENTIAL EVOLUTION FOR DYNAMIC OPTIMIZATION
SN - 978-989-8425-83-6
AU - Raciborski M.
AU - Trojanowski K.
AU - Kaczyński P.
PY - 2011
SP - 32
EP - 41
DO - 10.5220/0003671800320041