Swpmmas: an optimized parallel max-min ant system algorithm based on the SW26010-pro processor

The max-min ant system (MMAS) algorithm has found extensive application in tackling combinatorial optimization challenges such as the traveling salesman problem (TSP), production scheduling, and quadratic assignment. Nevertheless, as the scale of the problem increases, the MMAS algorithm gradually encounters performance limitations. To address the performance constraints of MMAS, we propose a parallel max-min ant system (PMMAS) algorithm, where a master subpopulation coordinates multiple subpopulations in parallel search. Furthermore, to facilitate the parallel acceleration of computationally intensive tasks in PMMAS using the CPE array of the SW26010-Pro processor, the selection weight calculation equation in the traditional MMAS algorithm was improved. This improvement led to the introduction of the Sunway parallel max-min ant system (SWPMMAS) algorithm, which implements parallelism using MPI and Athread. The revised selection weight calculation equation is also applicable to the traditional MMAS algorithm and enhances its running speed. Finally, the SWPMMAS algorithm was evaluated using various TSP instances, with city counts ranging from 51 to 11,849. The results demonstrate that the SWPMMAS algorithm provides excellent solutions. For TSP instances with more than 10,000 cities, the SWPMMAS algorithm achieves over 13 \(\times\) $\times$ speedup compared to the PMMAS algorithm running on the Sunway architecture and 5.4 \(\times\) $\times$ speedup compared to the PMMAS algorithm running on a commercial Shanhe supercomputer. Moreover, testing indicates that the SWPMMAS algorithm exhibits outstanding scalability.