Research and improvement of the real-coded chaotic quantum-inspired genetic algorithm

In order to overcome the disadvantages of the quantum genetic algorithm of premature and slow convergence, this paper propose a catastrophic real-coded chaotic quantum-inspired genetic algorithm, based on the continuous learning and accumulation of quantum genetic algorithm. Specific methods are adding convulsions, meanwhile, producing chaotic sequence with the Chebyshev mapping model, changing the crossover and mutation of the ratio of individual selection. The new algorithm overcomes early maturity, enhances optimization ability. The simulation results show that the algorithm has better effectiveness and rapid convergence.