Abstract: New methods were joined into the quantum genetic algorithm to solve the defects of poor local search ability and more iterative times. The best search direction was decided by the new swarm which was built by the best individual of each chromosome. Evolutionary strategy with niche was used to initialize quanta swarm. The simulation result of nonlinear continuous optimal problem indicates that the algorithm has better performance than quantum genetic algorithm. Finally, the algorithm was applied to chemical progress optimization and satisfactory results were achieved.

Key words: genetic algorithm, quantum genetic algorithm, niche

摘要： 针对量子遗传算法在多维复杂函数优化中迭代次数多、易陷入局部极值等缺点，提出新的量子遗传算法。通过搜索各种群中各染色体的最优个体，组成一个新的种群，并以此种群作为当前最优种群来确定量子门的全局最优搜索方向。引入小生境协同进化策略初始化量子种群，使量子染色体均匀分布于初值空间。以非线性连续优化问题为例所进行的仿真结果表明，该方法具有收敛速度快、寻优能力强等优点。最后，将该算法应用于化工过程的优化，取得良好的效果。

关键词: 遗传算法, 量子遗传算法, 小生境