Quantum-Inspired Differential Evolution for Binary Optimization

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 able to 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.