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Study of Pseudo-Parallel Genetic Algorithm with Ant Colony Optimization to Solve the TSP


Sheng Li, Huiqin Chen, Zheng Tang


Vol. 11  No. 3  pp. 73-79


The traveling salesman problem (TSP) has attracted many researchers’ attention in the past few decades, and amounts of algorithms based on heuristic algorithms, genetic algorithms, particle swarm optimization, tabu search and memetic algorithms have been presented to solve it, respectively. Unfortunately, their results have not been satisfied at all yet. This paper is devoted to the presentation of a novel hybrid pseudo-parallel genetic algorithm with ant colony optimization (PPGA-ACO). The experimental results on small and large size TSP instances in TSPLIB (traveling salesman problem library) show that PPGA-ACO is more robust and efficient than the traditional algorithms.


Traveling salesman, genetic algorithm, pseudo-parallel, ant colony optimization, hybridization