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Soft Computing

, Volume 20, Issue 10, pp 4111–4120 | Cite as

\(\mu \)JADE: adaptive differential evolution with a small population

  • Craig Brown
  • Yaochu Jin
  • Matthew Leach
  • Martin Hodgson
Methodologies and Application

Abstract

This paper proposes a new differential evolution (DE) algorithm for unconstrained continuous optimisation problems, termed \(\mu \)JADE, that uses a small or ‘micro’ (\(\mu \)) population. The main contribution of the proposed DE is a new mutation operator, ‘current-by-rand-to-pbest.’ With a population size less than 10, \(\mu \)JADE is able to solve some classical multimodal benchmark problems of 30 and 100 dimensions as reliably as some state-of-the-art DE algorithms using conventionally sized populations. The algorithm also compares favourably to other small population DE variants and classical DE.

Keywords

Micro differential evolution Small population External archive JADE 

Notes

Acknowledgments

The authors would like to thank the anonymous reviewers for helping to improve this paper.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Craig Brown
    • 1
  • Yaochu Jin
    • 2
  • Matthew Leach
    • 3
  • Martin Hodgson
    • 1
  1. 1.Bosch Thermotechnology Ltd.WorcesterUK
  2. 2.Department of ComputingUniversity of SurreyGuildfordUK
  3. 3.Centre for Environmental Strategy, Faculty of Engineering and Physical SciencesUniversity of SurreyGuildfordUK

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