基于自适应信息素调整的连续空间优化蚁群算法

Ant colony algorithm based on adaptively adjusting pheromone to continuous space optimizatio

对于基本蚁群算法(ACA)不适用求解连续空间问题,并且极易陷入局部最优的缺点,提出了一种基于自适应的蚁群算法。路径搜索策略采用基于目标函数值搜索筛选局部最优解的策略,确保能够迅速找到可行解。信息素更新策略采用自适应的启发式信息素分配策略,使算法能够快速收敛到全局最优解。对2个求函数极值问题进行优化并与其他算法进行比较,结果表明该算法能很好的应用于对连续对象的优化,同时具有较高的寻优精度高,搜索速率快,良好的全局优化性能。

According to the disadvantages that ant colony algorithm is not applied to continuous optimization problems and easy to get into local optimum,an improved ant colony algorithm adaptively is proposed.The path searching strategy adopts the value of the objective function to searching strategy based on the screening of local optimal solution,making sure to find feasible solution quickly.The pheromone update strategy adopts the heuristic information adaptive pheromone distribution strategy,makes the algorithm quickly converging to the global optimal solution.Extremum problem of the 2 continuous functions are optimized and compared with other algorithms.The testing result indicates that the improved algorithm is not only applied to continuous optimization problems,but also has fast global optimization,fast searching rate and high optimizing precision.