A hybrid chaotic quantum evolutionary algorithm

A hybrid chaotic quantum evolutionary algorithm is proposed to reduce amount of computation, speed up convergence and restrain premature phenomena of quantum evolutionary algorithm. The proposed algorithm adopts the chaotic initialization method to generate initial population which will form a perfect distribution in feasible solution space in advantage of randomicity and non-repetitive ergodicity of chaos, 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 the 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. Especially, the proposed algorithm is the only one that has 100% convergence rate in all tests. The presented algorithm is applied to urban traffic signal timing optimization and the effect is satisfied.