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Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization

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Abstract

In recent years, there has been a growing interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs). Among approaches developed for EAs to deal with DOPs, immigrants schemes have been proven to be beneficial. Immigrants schemes for EAs on DOPs aim at maintaining the diversity of the population throughout the run via introducing new individuals into the current population. In this paper, we carefully examine the mechanism of generating immigrants, which is the most important issue among immigrants schemes for EAs in dynamic environments. We divide existing immigrants schemes into two types, namely the direct immigrants scheme and the indirect immigrants scheme, according to the way in which immigrants are generated. Then experiments are conducted to understand the difference in the behaviors of different types of immigrants schemes and to compare their performance in dynamic environments. Furthermore, a new immigrants scheme is proposed to combine the merits of two types of immigrants schemes. The experimental results show that the interactions between the two types of schemes reveal positive effect in improving the performance of EAs in dynamic environments.

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Authors and Affiliations

  1. Nature Inspired Computation and Applications Laboratory, Department of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, 230027, China

    Xin Yu, Ke Tang, Tianshi Chen & Xin Yao

  2. CERCIA, The School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    Xin Yao

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  1. Xin Yu
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  2. Ke Tang
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  3. Tianshi Chen
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  4. Xin Yao
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Correspondence to Ke Tang.

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Yu, X., Tang, K., Chen, T. et al. Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization. Memetic Comp. 1, 3–24 (2009). https://doi.org/10.1007/s12293-008-0003-6

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