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

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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.

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Acknowledgments

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

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Correspondence to Yaochu Jin.

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Communicated by V. Loia.

This work was funded by Bosch Thermotechnology Ltd. and the Engineering and Physical Sciences Research Council (EPSRC) UK.

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Brown, C., Jin, Y., Leach, M. et al. \(\mu \)JADE: adaptive differential evolution with a small population. Soft Comput 20, 4111–4120 (2016). https://doi.org/10.1007/s00500-015-1746-x

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