Journal
IEEE ACCESS
Volume 8, Issue -, Pages 227497-227510Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3045765
Keywords
Traveling salesman problem; particle swarm optimization; mutation; particle competition mechanism; multi-subdomain grouping; k-opt
Categories
Funding
- Key Research and Development Project of Shandong Province [2018YFJH0704, 2019GHZ007]
- National Natural Science Foundation of China [51909127]
- Doctoral Scientific Research Starting Foundation of Qingdao University of Science and Technology
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Traveling salesman problem (TSP) has been widely applied to various fields of production and life. In order to solve the TSP, this work optimizes the architecture of the particle swarm optimization (PSO) by introducing a preprocessing multi-subdomain grouping approach to divide the entire search space into several sub-regions, based on the geographical center of planned cities. To solve subdomain problems, the global search ability of PSO is further improved using genetic mutation and particle competition mechanism to maintain the population diversity during late iterations. An effective simplified 4-opt is finally employed to eliminate path-crossing after connecting all subdomain paths in a time saving way. Comparison with three of our previous algorithms and seven algorithms from other reports reveals that the proposed algorithm shows good performance in both the computing efficiency and route quality. Particularly, it is suitable for small (or medium)-sized TSP problems with relatively percentage error values below 4.8%.
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