Abstract: This paper proposes and implements a new approach to parallel ant colony optimization (ACO) algorithms. The principal idea is to make multiple ant colonies share and utilize only one pheromone matrix. We call our approach sharing one pheromone matrix (SHOP). This paper briefly describes how to parallelize ACS and MMAS by SHOP strategy, and tries to hybridize these two in parallel. By tackling symmetric travelling salesman problems, this paper compares SHOP-ACO implementation with the relevant sequential ACO algorithms under fair computing environment, as well as with the existing parallel ACO algorithms. The experimental results indicate that SHOP strategy is superior to the sequential ACO algorithms and the existing parallel strategies.