An improved quantum-behaved particle swarm optimization algorithm

In this paper, we propose some improvements that enhance the optimization ability of quantum-behaved particle swarm optimization algorithms. First, we propose an encoding approach based on qubits described on the Bloch sphere. In our approach, each particle contains three groups of Bloch coordinates of qubits, and all three groups of coordinates are regarded as approximate solutions describing the optimization result. Our approach updates the particles using the rotation of qubits about an axis on the Bloch sphere. This updating approach can simultaneously adjust two parameters of qubits, and can automatically achieve the best matching of two adjustments. To avoid premature convergence, the mutation is performed with Hadamard gates. The optimization process is performed in the n-dimensional hypercube space ?1,1] ⁿ , so the proposed approach can be easily adapted to a variety of optimization problems. The experimental results show that the proposed algorithm is superior to the original one in optimization ability.