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量子进化方法是受量子计算思想的启发而产生的一种新型的高效算法,在计算效率和避免陷入局部极值问题上有着卓越的成效.因此,量子机制与智能优化算法的组合,将进一步扩展智能优化算法的应用领域,提高优化算法解决问题的能力.为此,将量子计算引入到差分进化算法中,提出一种新型的进化算法—量子差分进化算法.该方法将量子比特的概率幅表示应用于染色体的实数编码,用量子变异、量子交叉、量子选择操作实现染色体位置的更新,用量子非门进行量子位两个概率幅互换,能在防止算法早熟的同时使算法更快收敛.并分别以函数极值和TSP问题为例进行了仿真,验证了算法的有效性.
Quantum evolution method is a new and efficient algorithm inspired by the idea of quantum computation, and it has remarkable performance in calculating efficiency and avoiding falling into local extremum. Therefore, the combination of quantum mechanism and intelligent optimization algorithm will be further expanded Intelligent optimization algorithm to improve the ability of the optimization algorithm to solve the problem.Therefore, the quantum computing is introduced into the differential evolution algorithm, a new evolutionary algorithm - quantum differential evolution algorithm is proposed.This method presents the probability amplitude of the quantum bit It is applied to the real coding of chromosomes, and the update of chromosome positions is realized by quantum mutation, quantum crossover and quantum selection operations. Quantum bits can be used to exchange two probability frames with quantum non-gates, which can prevent algorithm premature convergence and make the algorithm converge faster. The extremum of function and TSP are respectively used as examples to verify the effectiveness of the algorithm.