基于遗传算法和蚂蚁算法求解函数优化问题

针对遗传算法求解精度低以及蚂蚁算法求解速度慢的问题，提出一种基于遗传算法和蚂蚁算法的混合算法.该混合算法利用了遗传算法快速随机的全局搜索能力的优点，设计了编码与适应度函数，进行了种群生成与染色体的选择，并通过设定交叉算子和变异算子， 生成了信息素分布.该混合算法利用了蚂蚁算法正反馈以及具有分布式并行全局搜索能力的优点，通过确定吸引强度的初始值，建立了强度更新的模型，从而求得精确解.并将该算法应用于求解函数优化问题.结果表明，该混合算法与遗传算法和蚂蚁算法相比，收敛速度快，寻优性能好.

Function optimization problem based on genetic algorithm and ant algorithm

Aimed at the problems of low solution precision by genetic algorithm and slow resolving speed by ant algorithm,a hybrid algorithm based on genetic algorithm and ant algorithm was presented.The merit of a quick and random global searching in genetic algorithm was used into the proposed hybrid algorithm.The coding and fitness function was designed,and population producing and chromosome selection were performed.The design of crossover operator and mutation operator was determined,and then pheromone distribution was achieved.The merits of positive feedback in ant algorithm,the parallel processing and global searching were utilized in the proposed hybrid algorithm,and the initial value of attraction intensity was confirmed.The model of intensity updating was set up,and then the exact solution was obtained.The algorithm was applied to solve function optimization problems.The results show that compared with genetic algorithm and ant algorithm,the proposed algorithm converges faster and has better searching ability.