Improving Differential Evolution With a Successful-Parent-Selecting Framework
An effective and efficient successful-parent-selecting framework is proposed to improve the performance of differential evolution (DE) by providing an alternative for the selection of parents during mutation and crossover. The proposed method adapts the selection of parents by storing successful sol...
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| Veröffentlicht in: | IEEE transactions on evolutionary computation 2015-10, Vol.19 (5), p.717-730 |
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| container_title | IEEE transactions on evolutionary computation |
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| creator | Shu-Mei Guo Chin-Chang Yang Pang-Han Hsu Tsai, Jason S.-H |
| description | An effective and efficient successful-parent-selecting framework is proposed to improve the performance of differential evolution (DE) by providing an alternative for the selection of parents during mutation and crossover. The proposed method adapts the selection of parents by storing successful solutions into an archive, and the parents are selected from the archive when a solution is continuously not updated for an unacceptable amount of time. The proposed framework provides more promising solutions to guide the evolution and effectively helps DE escaping the situation of stagnation. The simulation results show that the proposed framework significantly improves the performance of two original DEs and six state-of-the-art algorithms in four real-world optimization problems and 30 benchmark functions. |
| doi_str_mv | 10.1109/TEVC.2014.2375933 |
| format | Article |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Benchmark testing Differential evolution global numerical optimization Linear programming Optimization parent adaptation Sociology stagnation Statistics Upper bound Vectors |
| title | Improving Differential Evolution With a Successful-Parent-Selecting Framework |
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