  Differential evolution (DE) is a population based evolutionary algorithm
widely used for solving multidimensional global optimization problems over
continuous spaces. However, the design of its operators makes it unsuitable for
many real-life constrained combinatorial optimization problems which operate on
binary space. On the other hand, the quantum inspired evolutionary algorithm
(QEA) is very well suitable for handling such problems by applying several
quantum computing techniques such as Q-bit representation and rotation gate
operator, etc. This paper extends the concept of differential operators with
adaptive parameter control to the quantum paradigm and proposes the adaptive
quantum-inspired differential evolution algorithm (AQDE). The performance of
AQDE is found to be significantly superior as compared to QEA and a discrete
version of DE on the standard 0-1 knapsack problem for all the considered test
cases.
