Abstract:

A quantum shuffled frog leaping algorithm was proposed which combines with the quantum theory. In this algorithm, the individuals are expressed with Bloch spherical coordinates of qubits, the individual update is realized with the rotation of qubits in Bloch sphere, and the local search capabilities within the subgroup is improved with adaptive chaotic rotation angle operator. Then, to avoid premature convergence, the mutation of individuals is achieved with Hadamard gates. Above operations extend the search of the solution space effectively. Results of experiments show that compared with the SFLA, PSO and GA, the algorithm has a higher optimization capability and efficiency, and is more suitable for highdimensional optimization of complex functions.

Key words: quantum computing； shuffled frog leaping algorithm； continuous space optimizing； simulation