Quantum-Inspired Differential Evolution for Binary Optimization

Authors:
Haijun Su;Yupu Yang
Affiliations:
-;-
Venue:
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
Year:
2008

Citing 0
Cited 2

Classification rule discovery with DE/QDE algorithm

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Differential evolution and quantum-inquired differential evolution for evolving Takagi-Sugeno fuzzy models

Expert Systems with Applications: An International Journal

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Abstract

The differential evolution (DE) is usually considered as a robust, fast, powerful optimization approach. DE has been widely applied to solve many optimization problems in the continuous-valued space. However, DE is seldom used in the binary-valued space owing to its particular operators. The paper uses a Q-bit string as a representation, and proposes the quantum-inspired differential evolution algorithm (QDE). The operators of DE are used to be ableto drive the individuals to move to better solutions. Numerical experiments are performed to illustrate the performance of QDE compared with three algorithms in the binary-valued space. The results show that QDE generally outperform the other algorithms in the test functions.