Scalability of generalized adaptive differential evolution for large-scale continuous optimization |
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Authors: | Zhenyu Yang Ke Tang Xin Yao |
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Affiliation: | (1) Nature Inspired Computation and Applications Laboratory (NICAL), School of Computer Science and Technology, University of Science and Technology of China, Hefei, China;(2) School of Computer Science, University of Birmingham, Birmingham, UK |
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Abstract: | Differential evolution (DE) has become a very powerful tool for global continuous optimization problems. Parameter adaptations
are the most commonly used techniques to improve its performance. The adoption of these techniques has assisted the success
of many adaptive DE variants. However, most studies on these adaptive DEs are limited to some small-scale problems, e.g. with
less than 100 decision variables, which may be quite small comparing to the requirements of real-world applications. The scalability
performance of adaptive DE is still unclear. In this paper, based on the analyses of similarities and drawbacks of existing
parameter adaptation schemes in DE, we propose a generalized parameter adaptation scheme. Applying the scheme to DE results
in a new generalized adaptive DE (GaDE) algorithm. The scalability performance of GaDE is evaluated on 19 benchmark functions
with problem scale from 50 to 1,000 decision variables. Based on the comparison with three other algorithms, GaDE is very
competitive in both the performance and scalability aspects. |
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