基于交叉变异操作的连续域蚁群算法研究

研究一种基于交叉变异操作的连续域蚁群算法,该算法对解的每一分量的可能取值组成一个动态的候选组,并记录候选组中的每一个可能取值的信息量.在蚁群算法的每一次迭代中,首先根据信息量选择解分量的初值,然后使用交叉、变异操作来确定全局最优解的值,通过相应算法设计,对于来自相对适应度较大的解的分量值, 其变异的区域较小,成为局部搜索,反之,变异的区域较大,则构成全局搜索.同时,随着迭代次数的增多,分量值的变异幅度逐渐变小,这样可使收敛过程在迭代次数较多时得到适当的控制,以加速收敛.最后通过仿真实验,把交叉变异操作的连续域蚁群算法与遗传算法性能进行比较,证明了交叉变异操作的连续域蚁群算法具有较高的搜索较优解的能力,大大节约了计算时间.

Research Continual Domain Ant Colony Algorithm Based on Overlapping Mutation Operation

This paper studies a continual-domain ant colony algorithm based on the overlapping mutation operation, which forms a dynamic candidate group to the solution of each component possible value, and records each possibility value information content in the candidate group.In each iteration of ant colony algorithm, firstly, it should choose the starting value of solution component according to the information content, and then it should use overlapping and variation operation to determine the overall optimal so...