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A Fast Differential Evolution for Constrained Optimization Problems in Engineering Design

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Bio-Inspired Computing -- Theories and Applications (BIC-TA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 562))

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Abstract

A fast differential evolution (FDE) approach to solve several constrained engineering design optimization problems is proposed. In this approach, a new mutation strategy “DE/current-to-ppbest/bin” is proposed to get a balance between exploration and exploitation of the population. What’s more, a ranking based selection mechanism selects the promising individuals from the combination of parents and offspring to update the population. Experimental results on 5 instances extracted from engineering design show that FDE can acquire quite competitive performance. FDE is comparable to other state-of-the-art approaches in terms of solution quality. As for convergence speed, FDE is more fast, or at least comparable to, other state-of-the-art approaches. When the number of function evaluation is limited or the cost of function evaluation is expensive, FDE is a good choice.

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, China

    Aili Shen & Jinlong Li

Authors
  1. Aili Shen
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  2. Jinlong Li
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Corresponding author

Correspondence to Jinlong Li .

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Editors and Affiliations

  1. Xidian University, Xi'an, China

    Maoguo Gong

  2. Huazhong Univ. of Science and Technology, Wuhan, China

    Pan Linqiang

  3. University of Petroleum, Qingdao, China

    Song Tao

  4. University of Science and Technology of China, Hefei, China

    Ke Tang

  5. Anhui University, Hefei, China

    Xingyi Zhang

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Shen, A., Li, J. (2015). A Fast Differential Evolution for Constrained Optimization Problems in Engineering Design. In: Gong, M., Linqiang, P., Tao, S., Tang, K., Zhang, X. (eds) Bio-Inspired Computing -- Theories and Applications. BIC-TA 2015. Communications in Computer and Information Science, vol 562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49014-3_33

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  • DOI: https://doi.org/10.1007/978-3-662-49014-3_33

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49013-6

  • Online ISBN: 978-3-662-49014-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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