摘要
针对蚁群算法搜索时间长、易陷于局部最优解的缺点,提出一种增幅递减的局部信息素更新模型。通过分析现有蚁群算法信息素更新模型陷入局部最优的原因,借鉴蚁群模型退火算法思想,根据假设推导出增幅递减信息素更新模型,分析该模型对算法复杂度的影响,并分别采用4种信息素更新模型求解最短路问题。仿真结果表明,该模型能较好地抑制算法陷入局部最优解问题。
The ant colony algorithm search time is long and it is easy to fall into the local optimal. Put forward the amplitude descending local phenomenon renovating model. Through analyzing why the present algorithm fall into the local optimal, and using ant colony recombining algorithm, and according to hypothesis deduce amplitude descending local phenomenon renovating model, and analyze influence of model on algorithm complexity. Then use four pheromone renovating models to solve the shortest path problems. The simulation result shows that the model can restrain the algorithm to fall into the local optimal.
出处
《兵工自动化》
2012年第4期28-31,共4页
Ordnance Industry Automation
关键词
蚁群算法
增幅递减
局部最优
信息素变化
ant colony algorithm
amplitude descending
local optimal problem
growth changing
