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量子遗传算法的早熟问题影响算法的求解性能,为提高算法能力,提出基于混合蛙跳的量子遗传算法。算法引入混合蛙跳和模拟退火准则,采用量子变异策略;利用组内寻优和整体寻优,减少算法整体迭代次数。将改进后的量子遗传算法应用于函数优化方面,用测试函数的寻优来评价算法性能,实验结果表明,该算法有效提高了算法性能,能求解出符合要求的全局最优值,改善了早熟收敛的问题。
The premature problem of quantum genetic algorithm (GA) affects the performance of the algorithm. To improve the algorithm’s ability, a quantum genetic algorithm based on mixed frog leapfrog is proposed. The algorithm introduces mixed frog leaping and simulated annealing rules, adopts the quantum mutation strategy, and uses intra-group optimization and overall optimization to reduce the overall iteration number of the algorithm. The improved quantum genetic algorithm is applied to the function optimization, and the performance of the algorithm is evaluated by the optimization of the test function. Experimental results show that the algorithm can effectively improve the performance of the algorithm, can solve the global optimal value, and improve the precociousness The problem of convergence.