自适应并行机制的改进蚁群算法

针对蚁群算法存在停滞现象的缺点，以及如何有效提高蚂蚁代理的搜索能力问题，提出了一种具有自适应并行机制的选择和搜索策略。该策略通过将蚁群划分为若干个子群，不同子群的蚂蚁释放不同类型的信息素，引入了吸引因子和排斥因子，实现了一种多蚁群并行选择策略，以加强其全局搜索能力。以对称旅行商问题(traveling salesman problem, TSP)测试集为对象，将改进算法与现有蚁群优化算法进行了测试比较。实验结果表明，改进后的算法具有优良的全局优化能力，有效防止了停滞现象。

Ant colony algorithm with adaptive parallel mechanism

In view of the stagnation behavior of ant colony optimization (ACO) algorithm,this paper proposes and implements a new dynamic transition and search strategy.The artificial ants are partitioned into several groups.Each group of ant colony releases different types of pheromones.Attract factor and exclusion factor are introduced,and a new transition probability with multiple ant colony is given so as to strengthen the global search capability.By tackling symmetric travelling salesman problems (TSP),this paper compares the improved algorithms implementation with the existing algorithms.The experimental results indicate that the improved algorithm is superior to the ACO and ant colony system,ACS algorithms.The improved algorithm has excellent global optimization properties and the faster convergence speed,and it can avoid premature convergence of ACO.