Traveling salesman problem (TSP) is one of the typical NP-hard problems, and it has been used in
many engineering applications. However, the previous swarm intelligence (SI) based algorithms for TSP cannot
coordinate with the exploration and exploitation abilities and are easily trapped into local optimum. In order to
deal with this situation, a new hybrid optimization algorithm based on wolf pack search and local search (WPS-LS)
is proposed for TSP. The new method firstly simulates the predatory process of wolf pack from the broad field
to a specific place so that it allows for a search through all possible solution spaces and prevents wolf individuals
from getting trapped into local optimum. Then, local search operation is used in the algorithm to improve the
speed of solving and the accuracy of solution. The test of benchmarks selected from TSPLIB shows that the
results obtained by this algorithm are better and closer to the theoretical optimal values with better robustness
than those obtained by other methods.
DONG Ruyi (董如意), WANG Shengsheng *(王生生), WANG Guangyao (王光耀), WANG Xinying (王新颖)
. Hybrid Optimization Algorithm Based on Wolf Pack Search and Local Search for Solving Traveling Salesman Problem[J]. Journal of Shanghai Jiaotong University(Science), 2019
, 24(1)
: 41
-47
.
DOI: 10.1007/s12204-019-2039-9
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