Table 1.
A summary of some improved variants of the slime mold algorithm, including their names, improvement ideas, and the source of the algorithms.
Fig 1.
Slime mold search optimal solution and the CVRP solution path.
The nodes in the graph represent the possible optimal solutions in the slime mould search process; the nodes in the b graph represent the client nodes in the CVRP problem.
Fig 2.
The annealing mechanism is in the red dotted box.
Table 2.
Single-modal benchmark functions.
Table 3.
Multimodal benchmark function.
Table 4.
Algorithms-specific parameter settings.
Table 5.
Comparison results on benchmark functions with traditional algorithms.
Fig 3.
The convergence behavior of the comparative methods using CEC2013 problems.
F1-F7 are single-modal benchmark functions; F8-F13 are multimodal benchmark functions; and F14-F23 are composite benchmark functions.
Table 6.
Comparison of benchmark function results between traditional algorithms with 60 dimensions.
Table 7.
Comparison of benchmark function results between improved algorithms.
Table 8.
Comparison results on Wilcoxon rank sum test with algorithms.
Table 9.
Comparison results on Friedman’s ranking test with traditional algorithms.
Table 10.
Comparison results on Friedman’s ranking test with traditional algorithms with 60 dimensions.
Table 11.
Comparison results on Friedman’s ranking test with advanced algorithms.
Fig 4.
Fitness values obtained by SMA, SMA+SA, SMA+CM and SMA-CSA.
Fig 5.
Flowchart of SMA-CSA for CVRP.
Fig 6.
Swap example diagram.
Fig 7.
2-opt example diagram.
Fig 8.
3-opt example diagram.
Table 12.
Algorithms-specific parameter settings.
Table 13.
Comparison results on Christofides’s benchmark datasets of CVRP with advanced algorithms.
Table 14.
Comparison results on Golden’s benchmark datasets of CVRP with advanced algorithms.
Fig 9.
Comparison of results between SMA and SMA-CSA in C5 and GWKC4 examples.
Fig 10.
Results when one of the local search or mutation strategies is unused on C5 and GWKC4.