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Two improved differential evolution schemes for faster global search

Published:25 June 2005Publication History

ABSTRACT

Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. In this paper we present two new, improved variants of DE. Performance comparisons of the two proposed methods are provided against (a) the original DE, (b) the canonical particle swarm optimization (PSO), and (c) two PSO-variants. The new DE-variants are shown to be statistically significantly better on a seven-function test bed for the following performance measures: solution quality, time to find the solution, frequency of finding the solution, and scalability.

References

  1. Angeline, P. J. Evolutionary optimization versus particle swarm optimization: Philosophy and the performance difference, Lecture Notes in Computer Science (vol. 1447), Proceedings of 7th International Conference on. Evolutionary Programming - Evolutionary Programming VII (1998) 84--89. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Blackwell, T. A., Bentley, P. Improvised music with swarms, In Proceedings of IEEE Congress on Evolutionary Computation 2002, vol. 2, Honolulu, HI, (2002) 1462--1467. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Eberhart, R. C., Shi, Y. Particle swarm optimization: Developments, applications and resources, In Proceedings of IEEE International Conference on Evolutionary Computation, vol. 1, (2001) 81--86.Google ScholarGoogle Scholar
  4. Eberhart, R. C., Shi, Y. Comparing inertia weights and constriction factors in particle swarm optimization, Proceedings of IEEE International Congress on Evolutionary Computation, vol. 1, (2000) 84--88.Google ScholarGoogle ScholarCross RefCross Ref
  5. Kennedy, J., Eberhart, R. Particle swarm optimization, In Proceedings of IEEE International Conference on Neural Networks, (1995) 1942--1948.Google ScholarGoogle Scholar
  6. Kennedy, J. Stereotyping: Improving particle swarm performance with cluster analysis, Proc. IEEE Int. Conf. Evolutionary Computation, vol. 2, (2000) 303--308.Google ScholarGoogle ScholarCross RefCross Ref
  7. Shi, Y., Eberhart, R. C. Comparison between genetic algorithm and particle swarm optimization, Lecture Notes in Computer Science -- Evolutionary Programming VII, vol. 1447, (1998) 611--616. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Shi, Y., Eberhart, R. C. Parameter selection in particle swarm optimization, Lecture Notes in Computer Science Evolutionary Programming VII, vol. 1447, (1998) 591--600. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Shi, Y., Eberhart, R. C. Empirical study of particle swarm optimization. In Proceedings of IEEE International Conference on Evolutionary Computation, vol 3, (1999) 101--106.Google ScholarGoogle ScholarCross RefCross Ref
  10. Storn, R., Price, K. Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, 11(4) (1997) 341--359. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. van den Bergh, F., Engelbrecht, P. A. Effects of swarm size on cooperative particle swarm optimizers, In Proceedings of GECCO-2001, San Francisco, CA, (2001) 892--899.Google ScholarGoogle Scholar
  12. Yao, X., Liu, Y., Lin, G. Evolutionary programming made faster, IEEE Transactions on Evolutionary Computation, vol 3, No 2, (1999) 82--102. Google ScholarGoogle ScholarDigital LibraryDigital Library

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                cover image ACM Conferences
                GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation
                June 2005
                2272 pages
                ISBN:1595930108
                DOI:10.1145/1068009

                Copyright © 2005 ACM

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                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 25 June 2005

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