A New Fast Ant Colony Optimization Algorithm: The Saltatory Evolution Ant Colony Optimization Algorithm
A New Fast Ant Colony Optimization Algorithm: The Saltatory Evolution Ant Colony Optimization Algorithm
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一种新的快速蚁群优化算法：跳跃进化蚁群优化算法
DOI：
10.3390/math10060925
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发表时间：
2022-03-01
期刊：
影响因子：
2.4
通讯作者：
Yu, Zhaoxu
中科院分区：
文献类型：
--
作者：
Li, Shugang;Wei, Yanfang;Yu, Zhaoxu
各种研究表明，蚁群算法在逼近实际应用中的旅行商问题等复杂组合优化问题时具有较好的性能。然而，运行时间长、易停滞等缺点仍然限制了其在许多领域的进一步广泛应用。为了提高算法的寻优速度，提出了一种跳跃式进化蚁群算法。与以往的研究不同，本研究创新性地从近优路径识别的角度出发，利用传统蚁群算法的信息素矩阵进化数据，通过定量分析模型对近优路径识别领域知识进行提炼。基于领域知识，建立近优路径预测模型，预测路径信息素矩阵的演化趋势，从根本上节省运行时间。在旅行商问题库(TSPLIB)上的大量实验结果表明，SeaCo算法的解质量优于蚁群算法，更适合于指定时间窗口内的大规模数据集。这为解决蚁群算法寻优速度慢、精度低的问题提供了一个很有前途的方向。
Various studies have shown that the ant colony optimization (ACO) algorithm has a good performance in approximating complex combinatorial optimization problems such as traveling salesman problem (TSP) for real-world applications. However, disadvantages such as long running time and easy stagnation still restrict its further wide application in many fields. In this study, a saltatory evolution ant colony optimization (SEACO) algorithm is proposed to increase the optimization speed. Different from the past research, this study innovatively starts from the perspective of near-optimal path identification and refines the domain knowledge of near-optimal path identification by quantitative analysis model using the pheromone matrix evolution data of the traditional ACO algorithm. Based on the domain knowledge, a near-optimal path prediction model is built to predict the evolutionary trend of the path pheromone matrix so as to fundamentally save the running time. Extensive experiment results on a traveling salesman problem library (TSPLIB) database demonstrate that the solution quality of the SEACO algorithm is better than that of the ACO algorithm, and it is more suitable for large-scale data sets within the specified time window. This means it can provide a promising direction to deal with the problem about slow optimization speed and low accuracy of the ACO algorithm.