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基于协同策略和量子免疫计算理论,提出量子协同免疫动态优化算法,并从理论上证明算法的全局收敛性.该算法采用量子比特编码表达种群中的抗体,并采用量子旋转门和动态调整旋转步长策略来演化抗体,加速原有克隆算子的收敛.该算法中引入协同策略增强子群体间的信息交流,提高种群的多样性,同时利用量子编码种群的关联性,使算法具有更强的稳定性,能够较好地适应于动态问题的求解.文中通过一系列动态背包测试问题和交叉验证(t检验)实验表明,量子协同免疫动态优化算法具有更强的鲁棒性和适应性,显示出较优越的性能.
Based on the collaborative strategy and quantum immune computing theory, a dynamic optimization algorithm of quantum co-immunization is proposed and the global convergence of the algorithm is proved theoretically. The algorithm uses quantum bit coding to express the antibodies in the population, and adopts quantum revolving door and dynamic adjustment rotation step Long strategy to evolve the antibody to accelerate the convergence of the original clonal operator.This algorithm introduces a collaborative strategy to enhance the exchange of information between subgroups to improve the diversity of the population and make the algorithm stronger by using the relevance of the quantum code population Stability and can be well adapted to solving dynamic problems.Through a series of dynamic backpack test problems and cross-validation (t-test) experiments show that the quantum coordinated immune dynamic optimization algorithm has a stronger robustness and adaptability, showing Out of superior performance.