In recent years, the use of compute-intensive coprocessors has been widely studied in the field of Parallel Computing to accelerate sequential processes through a Graphic Processing Unit (GPU). Intel has recently released a GPU-type coprocessor, the Intel Xeon Phi. It is composed up to 72 cores connected by a bidirectional ring network with a Vector Process Unit (VPU) on large vector registers. In this work, we present novel parallel algorithms of the well-known Ant Colony Optimization (ACO) on the recent many-core platform Intel Xeon Phi coprocessor. ACO is a popular metaheuristic algorithm applied to a wide range of NP-hard problems. To show the efficiency of our approaches, we test our algorithms solving the Traveling Salesman Problem. Our results confirm the potential of our proposed algorithms which led to distinct improvements of performance over previous state-of-the-art approaches in GPU. We implement and compare a set of algorithms to deal with the different steps of ACO. The matrices calculation in the proposed algorithms efficiently exploit the VPU and cache in Xeon Phi. We also show a novel implementation of the roulette wheel selection algorithm, named as UV-Roulette (unique random value roulette). We compare our results in Xeon Phi to state-of-the-art GPU methods, achieving higher performance with large size problems. We also exposed the difficulties and key hardware performance factors to deal with the ACO algorithm on a Xeon Phi coprocessor.