Institute of Transportation, MOTC

　　In this research, a particle swarm optimization algorithm was employed, coupled with the techniques of a genetic algorithm, and threshold acceptance method and concave cost network heuristics, to develop a path-based global search algorithm for efficiently solving minimum cost network flow problems with square root concave arc costs. To evaluate the proposed algorithm, several network flow problems are randomly generated. C++ is used to code all the necessary programs for the tests. The results indicate that the proposed algorithm is more effective than recently designed local search algorithms and genetic algorithms for solving minimum cost network flow problems with square root concave arc costs.