Hybrid Optimization Algorithm Based on Wolf Pack Search and Local Search for Solving Traveling Salesman Problem

  • Ruyi Dong (董如意)
  • Shengsheng Wang (王生生)Email author
  • Guangyao Wang (王光耀)
  • Xinying Wang (王新颖)


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.

Key words

traveling salesman problem (TSP) swarm intelligence (SI) wolf pack search (WPS) combinatorial optimization 

CLC number

TP 18 

Document code


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Copyright information

© Shanghai Jiaotong University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Ruyi Dong (董如意)
    • 1
    • 2
  • Shengsheng Wang (王生生)
    • 1
    Email author
  • Guangyao Wang (王光耀)
    • 1
  • Xinying Wang (王新颖)
    • 3
  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Jilin Vocational College of Industry and TechnologyJilinChina
  3. 3.College of Computer Science and EngineeringChangchun University of TechnologyChangchunChina

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