Abstract To solve low convergence precision and slow convergence speed, a pseudo-dynamic search ant colony optimization algorithm with improved negative feedback mechanism (PACON) is proposed. Firstly, the algorithm introduces an angle in the pheromone transfer rule. Through the rule for calculating the angle, multiple cities with smaller angles are also included in the next candidate city list. It affects the probability of city selection and enhances the algorithm’ performance to avoid local optimization. Secondly, the algorithm updates the pheromone concentrations on the worst and optimal path simultaneously, and enhances the weights of the pheromone concentrations on the optimal path. It improves the convergence speed of the algorithm. Based on experiments adopting TSPLIB data sets, the results demonstrate the improved algorithm improves the convergence accuracy by at least 1.26% and increases the convergence speed by at least 9.5%, both on large-scale and small-scale urban data. The novel algorithm will improve convergence precision and speed better.

中文翻译：

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一种具有改进负反馈机制的伪动态搜索蚁群优化算法

摘要 针对收敛精度低、收敛速度慢的问题，提出了一种改进负反馈机制的伪动态搜索蚁群优化算法（PACON）。首先，该算法在信息素传递规则中引入了一个角度。通过计算角度的规则，多个角度较小的城市也会被列入下一个候选城市列表。它影响城市选择的概率并提高算法的性能以避免局部优化。其次，算法同时更新最差和最优路径上的信息素浓度，并增强最优路径上信息素浓度的权重。提高了算法的收敛速度。基于采用TSPLIB数据集的实验，结果表明，无论是大尺度还是小尺度城市数据，改进算法的收敛精度至少提高了1.26%，收敛速度至少提高了9.5%。新算法将更好地提高收敛精度和速度。