An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization
Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/969734 |
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| Summary: | Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size
(NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed
adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although
there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt
the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm.
In the proposed algorithm, each individual has its own control parameters. The control parameters of each
individual are adapted based on the average parameter value of successfully evolved individuals’ parameter
values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned
either near the average parameter value or far from that of the average parameter value which might be
better parameter value for next generation. The experimental results show that the proposed algorithm
is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving
various unimodal and multimodal problems. |
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| ISSN: | 1537-744X |