CAJ | 학술논문

为克服传统蚁群系统( ACS)在较大规模问题计算中易陷入局部最优，以及求解精度较低等不足，提出一种新的改进蚁群算法。该算法引入最小1-树中的α-邻近概念，能更好地反映给定边属于最优回路的概率，通过转换邻接矩阵，计算出最优回路的下界，以此提高α值的精度，并给出适应性探索策略，加入3-opt领域搜索算子，有效提高优化解的精度。实验结果表明，该算法具有更好的全局寻优能力，与 ACS 等算法相比能获得更加优化的解。
위극복전통의군계통( ACS)재교대규모문제계산중역함입국부최우，이급구해정도교저등불족，제출일충신적개진의군산법。해산법인입최소1-수중적α-린근개념，능경호지반영급정변속우최우회로적개솔，통과전환린접구진，계산출최우회로적하계，이차제고α치적정도，병급출괄응성탐색책략，가입3-opt영역수색산자，유효제고우화해적정도。실험결과표명，해산법구유경호적전국심우능력，여 ACS 등산법상비능획득경가우화적해。
In order to overcome the disadvantages that traditional Ant Colony System( ACS) is easy to fall into local optimum and low-precision for large-scale problems,this paper presents an improved ant colony optimization algorithm. The algorithm introduces α-nearest in the minimum 1-tree, which better reflects the chances of a given link being a member of an optimal tour. It improves α-nearest precision by transforming the adjacency matrix and computes a lower bound. Besides,it proposes the adaptive exploration strategy and 3-opt local search. Experimental results show that this algorithm has a better global searching ability in finding the best solutions and can obtain better solutions than ACS,etc.