混合混沌量子进化算法

针对量子进化算法计算量大、收敛速度慢以及容易出现早熟等问题, 提出混合混沌量子进化算法. 该算法采用混沌 初始化方法产生初始种群, 使种群具有较好的多样性；采用简单量子旋转门更新当前种群中的非最优个体, 降低算法的计算量； 提出混合混沌搜索策略以提高算法的收敛速度和全局搜索能力. 大量的测试表明, 与量子进化算法、实数编码量子进化算法和 混合量子遗传算法相比, 所提出的算法具有较快的收敛速度和较好的寻优能力. 大量的测试也表明, 若将混沌引入量子进化算法, 则混合混沌搜索策略的综合性能明显优于载波混沌策略, 在大多数情况下优于混沌变异策略. 本文提出的算法是惟一的每次测试 都收敛的算法, 且实现简单, 便于工程应用. 将其用于求解城市道路的交通信号配时优化问题, 实际效果令人满意.

Hybrid chaotic quantum evolutionary algorithm

In order to reduce amount of computation,speed up convergence and restrain premature phenomena of quantum evolutionary algorithm,a hybrid chaotic quantum evolutionary algorithm is presented. The algorithm uses the chaotic initialization method to generate initial population that have better diversity, the simple quantum rotation gate to update non-optimal individuals of population to reduce amount of computation,and the hybrid chaotic search strategy to speed up its convergence and enhance its global search ability.A large number of tests show that the proposed algorithm has higher convergence speed and better optimizing ability than quantum evolutionary algorithm,real-coded quantum evolutionary algorithm and hybrid quantum genetic algorithm.Tests also show that when chaos is introduced to quantum evolutionary algorithm,the hybrid chaotic search strategy is superior to the carrier chaotic strategy,and has better comprehensive performance than the chaotic mutation strategy in most of cases.The proposed algorithm is the only one all of whose tests are convergent,and is easily implemented and applied in practice.It is satisfied in solving traffic signal timing optimization problem of urban road.