A quantum-inspired evolutionary algorithm based on culture and knowledge

Quantum-inspired evolutionary algorithm has premature and slow convergence shortcomings on solving numerical optimization problems. To overcome these shortcomings, a novel quantum-inspired evolutionary algorithm based on culture & knowledge is proposed by introducing the cultural algorithm. This algorithm contains two evolutionary layers: quantum evolutionary layer and knowledge evolutionary layer. Since the introduction of cultural algorithm, this algorithm can achieve fine balance between exploration and exploitation as well as can escape from local optimum. Because of the new framework and quantum observation, the proposed algorithm not only retains the advantages of quantum coding, but also effectively solves numerical optimization problems. The experimental results show that the algorithm has better performance than the quantum-inspired evolutionary algorithms. The proposed algorithm performs better than other related algorithms in terms of speed and accuracy. ©, 2015, Systems Engineering Society of China. All right reserved.