摘要
A quantum evolutionary computation （QEC） algorithm with particle swarm optimization （PSO） and two-crossovers is proposed to overcome identified limitations. PSO is adopted to update the Q-bit automatically, and two-crossovers are applied to improve the convergence quality in the basic QEC model. This hybrid strategy can effectively employ both the ability to jump out of the local minima and the capacity of searching the global optimum. The performance of the proposed approach is compared with basic QEC on the standard unconstrained scalable benchmark problem that numerous hard combinatorial optimization problems can be formulated. The experimental results show that the proposed method outperforms the basic QEC quite significantly.
A quantum evolutionary computation （QEC） algorithm with particle swarm optimization （PSO） and two-crossovers is proposed to overcome identified limitations. PSO is adopted to update the Q-bit automatically, and two-crossovers are applied to improve the convergence quality in the basic QEC model. This hybrid strategy can effectively employ both the ability to jump out of the local minima and the capacity of searching the global optimum. The performance of the proposed approach is compared with basic QEC on the standard unconstrained scalable benchmark problem that numerous hard combinatorial optimization problems can be formulated. The experimental results show that the proposed method outperforms the basic QEC quite significantly.
基金
Supported by the National Natural Science Foundation of China under Grant Nos 60975072 and 60604009, Aeronautical Science Foundation of China under Grant Nos 2008ZC01006 and 2006ZC51039, and Beijing NOVA Program Foundation under Grant Nos 2007A017.
