As a novel computational paradigm, Physarum solver has received increasing attention from the researchers in tackling a plethora of network optimization problems. However, the convergence of Physarum solver is grounded by solving a system of linear equations iteratively, which often leads to low computational performance. Two factors have been highlighted along the process: 1) high time complexity in solving the system of linear equations and 2) extensive iterations required for convergence. Thus, Physarum solver has been largely restricted by its unsatisfactory computational performance. In this paper, we aim to address these two issues by developing two enhancement strategies: 1) pruning inactive nodes and 2) terminating Physarum solver in advance. First, extensive nodes and edges become and stay inactive after a few iterations in identifying the shortest path. Removing these inactive nodes and edges significantly decreases the graph size, thereby reducing computational complexity. Second, we define a transition phase for edges. All of the paths experiencing such a transition phase are dynamically aggregated to form a set of near-optimal paths among which the optimal path is included. Depth-first search is then leveraged to identify the optimal path from the near-optimal paths set. Earlier termination of Physarum solver saves considerable iterations while guaranteeing the optimality of the found solution. Empirically, 20 randomly generated sparse and complete graphs with network sizes ranging from 50 to 2000 as well as two real-world traffic networks are used to compare the performance of accelerated Physarum solver to the other two state-of-the-art algorithms.

中文翻译：

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用于网络优化的加速型Physarum解算器

作为一种新颖的计算范式，Physarum求解器在解决众多网络优化问题方面受到了研究人员的越来越多的关注。但是，Physarum求解器的收敛是通过迭代求解线性方程组系统而建立的，这通常会导致较低的计算性能。在此过程中，突出了两个因素：1）求解线性方程组的时间复杂性高； 2）收敛所需的大量迭代。因此，Physarum求解器一直受到其不令人满意的计算性能的限制。在本文中，我们旨在通过开发两种增强策略来解决这两个问题：1）修剪不活动的节点和2）提前终止Physarum求解器。第一的，在确定最短路径时，经过几次迭代后，广泛的节点和边缘变为并保持不活动状态。删除这些不活动的节点和边缘会大大减小图形的大小，从而降低计算复杂度。其次，我们定义边缘的过渡阶段。动态地经历所有经历这种过渡阶段的路径，以形成一组接近最佳的路径，其中包括最佳路径。然后利用深度优先搜索从接近最佳的路径集中识别出最佳路径。提前终止Physarum求解器可以节省大量迭代，同时保证所找到解决方案的最优性。根据经验，