Gulcu, SabanMahi, MostafaBaykan, Omer KaanKodaz, Halife2020-03-262020-03-2620181432-76431433-7479https://dx.doi.org/10.1007/s00500-016-2432-3https://hdl.handle.net/20.500.12395/36278This article presented a parallel cooperative hybrid algorithm for solving traveling salesman problem. Although heuristic approaches and hybrid methods obtain good results in solving the TSP, they cannot successfully avoid getting stuck to local optima. Furthermore, their processing duration unluckily takes a long time. To overcome these deficiencies, we propose the parallel cooperative hybrid algorithm (PACO-3Opt) based on ant colony optimization. This method uses the 3-Opt algorithm to avoid local minima. PACO-3Opt has multiple colonies and a master-slave paradigm. Each colony runs ACO to generate the solutions. After a predefined number of iterations, each colony primarily runs 3-Opt to improve the solutions and then shares the best tour with other colonies. This process continues until the termination criterion meets. Thus, it can reach the global optimum. PACO-3Opt was compared with previous algorithms in the literature. The experimental results show that PACO-3Opt is more efficient and reliable than the other algorithms.en10.1007/s00500-016-2432-3info:eu-repo/semantics/closedAccessAnt colony optimizationParallel algorithm3-Opt algorithmTraveling salesman problemMaster-slave paradigmArticle22516691685Q2WOS:000426566400025Q2