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Soft Computing

, Volume 15, Issue 11, pp 2157–2174 | Cite as

Self-adaptive differential evolution algorithm using population size reduction and three strategies

  • Janez Brest
  • Mirjam Sepesy Maučec
Focus

Abstract

Many real-world optimization problems are large-scale in nature. In order to solve these problems, an optimization algorithm is required that is able to apply a global search regardless of the problems’ particularities. This paper proposes a self-adaptive differential evolution algorithm, called jDElscop, for solving large-scale optimization problems with continuous variables. The proposed algorithm employs three strategies and a population size reduction mechanism. The performance of the jDElscop algorithm is evaluated on a set of benchmark problems provided for the Special Issue on the Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. Non-parametric statistical procedures were performed for multiple comparisons between the proposed algorithm and three well-known algorithms from literature. The results show that the jDElscop algorithm can deal with large-scale continuous optimization effectively. It also behaves significantly better than other three algorithms used in the comparison, in most cases.

Keywords

Differential evolution Self-adaptation Large-scale optimization Multiple statistical comparison 

Notes

Acknowledgments

The authors would like to thank the organizers of this special issue and the reviews for their valuable comments.

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and Computer ScienceUniversity of MariborMariborSlovenia

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