基于概率幅编码的混合量子演化算法

Amplitude-based coding hybrid quantum-inspired evolutionary algorithm

个体基于量子概率幅进行编码,并将经典遗传算法的杂交算子用于量子演化算法中演化目标的优化,提出了混合量子演化算法。算法中对量子旋转角自适应更新,并首次引入了突变度的概念定义了自适应的变异算子,对量子个体的演化目标定期实施杂交,有效地交换并利用了演化信息,避免了未成熟收敛,提高了算法效率。数值优化问题的实验结果表明该算法优于QEA和CGA,并能以极大概率成功地解决“大海捞针”问题,且计算效率高,优化速度与CGA相当。

Coding the individual with amplitude,and applying the crossover operator of classical genetic algorithm to the evolutionary goals of quantum-inspired evolutionary algorithm,a hybrid quantum-inspired evolutionary algorithm is proposed.Combining the self-adaptive rotation,defining a self-adaptive mutation operator with respect to mutation degree,and exchanging the informa- tion of the evolutionary goals by crossover operator regularly,the novel algorithm avoids converging prematurely and yields high efficiency.Also the algorithm exceeds QEA and CGA when they solve the numerical optimization problems.The Need-in-a- Haystack problem can be figured out perfectly.Additionally,the algorithm＇s computing speed is similar to the classical one.