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Differential evolution algorithm directed by individual difference information between generations and current individual information

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Abstract

In differential evolution (DE) algorithm, numerous adaptive methods based on superior individual information in the current generation have been proposed. However, the individual difference between two generations, which represents whether the corresponding parameters and mutation strategy are suitable for this individual, has not been utilized. Considering that different (superior or inferior) individuals need different parameters and strategies, a new DE variant (DI-DE), which is directed by individual difference information between generations and individual information in the current generation to obtain optimal control parameters and an offspring generation strategy, is proposed. In DI-DE, every individual possesses its own parameters and strategy. First, individuals are distinguished as superior or inferior depending on their fitness values in the current generation. The parameters are tuned in accordance with the information on superior individuals. In addition, the conception of potential individuals is proposed for superior and inferior individuals on the basis of the individual difference information between two generations. By learning from the current and past information, the suitable mutation strategy is adjusted for superior and inferior individuals on the basis of the experience of potential individuals to help them become potential individuals in the next generation. DI-DE is compared with 28 excellent algorithms on three well-known benchmark sets (CEC2005, CEC2013, and CEC2014) of low dimensionality and one large scale benchmarks set (CEC LSGO 2013). Experimental results demonstrate the competitive performance of DI-DE. Finally, DI-DE is applied to optimize the operation conditions of PX oxidation process.

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Acknowledgments

The authors are grateful for the support of the 973 Project of China (2013CB733600), and Fundamental Research Funds for the Central Universities under Grant of China (222201717006).

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Authors and Affiliations

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai, 200237, People’s Republic of China

    Li Tian, Zhichao Li & Xuefeng Yan

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  1. Li Tian
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Correspondence to Xuefeng Yan.

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Tian, L., Li, Z. & Yan, X. Differential evolution algorithm directed by individual difference information between generations and current individual information. Appl Intell 49, 628–649 (2019). https://doi.org/10.1007/s10489-018-1255-6

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