基于量子免疫遗传算法的火力分配优化问题

Fire distribution and optimization based on quantum immune genetic algorithm

针对传统方法在解决火力分配优化问题时存在迭代次数多、收敛速度慢、易陷入局部极值等不足,将免疫遗传算法中的免疫克隆、免疫记忆、免疫平衡机制引入到量子遗传算法中,利用求解问题的先验知识和局部最优解信息来改善和优化量子遗传算法的性能,提高了算法的收敛精度、收敛速度和稳定性。在分析问题背景和算法实现过程的基础上,通过实例仿真,模拟了不同容量的抗体记忆库对算法性能的影响,对比了普通遗传算法、量子遗传算法、免疫遗传算法以及文中所提及的量子免疫遗传算法在解决火力分配优化问题上的不同优化效果,结果表明:该方法在解决火力分配问题时,可以有效克服早熟现象,具有收敛速度较快、稳定性较好的特性。

In order to enhance the precision and stability of the quantum genetic algorithm ,the mecha-nism of immune clone ,immune memory and immune balance in the immune genetic algorithm is intro-duced into the quantum genetic algorithm .The problem-solving priori knowledge and local optimal in-formation are used to improve the property of the quantum genetic algorithm ,including an increase in its convergent precision and speed and its stability .In the experiments ,the effects of the antibody memory banks with different capacity on the property of the algorithm are simulated and ,the quantum immune genetic algorithm mentioned in this paper is compared with other algorithms such as the com-mon genetic algorithm ,quantum genetic algorithm ,immune and genetic algorithm as far as the differ-ent effects of fire distribution and optimization are concerned .The results indicate that the quantum immune genetic algorithm is more effective in solving the problem of fire distribution and optimization .