Adaptive Grouping Quantum Inspired Shuffled Frog Leaping Algorithm

--------------------------------------------------------ABSTRACT----------------------------------------------------------To enhance the optimization ability of classical shuffled frog leaping algorithm, a quantum inspired shuffled frog leaping algorithm with adaptive grouping is proposed. In this work, the frog swarms are adaptive grouped according to the average value of the objective function of child frog swarms, the frogs are encoded by probability amplitudes of Multi-Qubits system. The rotation angles of Multi-Qubits are determined based on the local optimum frog and the global optimal frog, and the Multi-Qubits rotation gates are employed to update the worst frog in child frog swarms. The experimental results of some benchmark functions optimization shows that, although its single step iteration consumes a long time, the optimization ability of the proposed method is significantly higher than the classical leaping frog algorithm.