基于遗传算法求解应急决策系统中的最优路径

Best path analysis of emergency decision system based on improved genetic algorithm

提出了一种将模拟退火算法和遗传算法相结合的进化算法GASA,利用Boltzmann机制 接收交叉和变异后的个体,避免遗传算法中存在的早熟收敛问题,增强了算法的全局收敛性,并对遗 传算子(选择、交叉、变异算子)进行重构,引入新的交叉算子和变异算子能根据种群的进化情况动态 调整遗传算子,加速进化后期搜索效率。实验表明,将此算法用于应急决策系统的最优路径的求解中 与传统算法相比,能加速进化速度和全局寻优能力,提高应急决策效率。

An improved genetic algorithm,evolving algorithm GASA was proposed,in which genetic algorithm was combined with simulated annealing algorithm. It avoided the premature convergence problem existed in Genetic Algorithm by useing Boltamann,and enhanced the global convergence. Genetic operators was redesigned, such as selection operator, cross operator and variation operator,on genetic algorithm. New cross operator and variation operator was proposed, which could dynamically regulate genetic operator according to evolving situation of groups. The algorithm was used in the best path problem of emergency decision support system,and it is proved to be reasonable and efficient.