An Improved Quantum Genetic Algorithm and Performance Analysis

Aiming at the drawback of being easily trapped into the local optimum and premature convergence in quantum genetic algorithm, an improved quantum genetic algorithm was proposed. Some worse individuals that were far from the population center were selected into personal best population in order to maintain population diversity. In the evolutionary process of population, adaptive adjustment of population diversity coefficient balanced exploration and exploitation. The simulation results of testing standard benchmark functions demonstrate that improved quantum genetic algorithm has the best optimization performance and robustness, the validity and feasibility of the method are verified.