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International Conference on Intelligent Computing

ICIC 2018: Intelligent Computing Theories and Application pp 669–680Cite as

iCorr-GAA Algorithm for Solving Complex Optimization Problem

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10955))

Abstract

Optimization is widely used to solve problems in many fields. With the development of society, the complexity of optimization problems is also increasing. Genetic algorithm (GA) is one of the most powerful stochastic optimizer. As a well-known GA variant, Correlation-based Genetic Algorithm (Corr-GAA) has been successfully applied to solve these optimization problems. Although highly effective, Corr-GAA tends to converge quickly at early evolution, and may fall into the local optimum in the later evolution stage. Non-uniform mutation operator can effectively improve this situation by adjusting dynamically search step of each iteration. In this paper we present an improved genetic algorithm (iCorr-GAA) that combines Corr-GAA with non-uniform mutation operator to solve complex optimization problems. The performance of the algorithm was evaluated by solving a set of benchmark functions provided for CEC 2014 special session and competition. Experimental results give evidence that iCorr-GAA has good global search capability and fast convergence speed.

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Acknowledgement

The project was partly sponsored by Guangdong province science and technology planning projects (Grant: 2016B070704010), and Guangdong province science and technology planning projects (Grant: 2016B010124010).

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Authors and Affiliations

  1. School of Software Engineering, South China University of Technology, Guangzhou, 510000, Guangdong, China

    Fangyuan Ding, Min Huang, Yongsheng Deng & Han Huang

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  1. Fangyuan Ding
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  2. Min Huang
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  3. Yongsheng Deng
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  4. Han Huang
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Corresponding author

Correspondence to Min Huang .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Wuhan University of Science and Technology, Wuhan City, China

    Xiao-Long Zhang

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Ding, F., Huang, M., Deng, Y., Huang, H. (2018). iCorr-GAA Algorithm for Solving Complex Optimization Problem. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_76

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  • DOI: https://doi.org/10.1007/978-3-319-95933-7_76

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95932-0

  • Online ISBN: 978-3-319-95933-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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