Based on the analysis of exist ant colony optimization (ACO) algorithms and the studies in visual perception and cognitive psychology, this paper proposes a new optimization strategy, the visual feedback and behavioral memory based Max-Min ant colony optimization algorithm (VM-MMACO). The main idea is to enhance the ant’s search ability by establishing the learning mechanism of visual feedback and behavioral memory. With artificial visual memory and learning abilities, the ant can not only see the targets around, using visual perception to optimize the heuristic information produced by pheromone in order to improve the search quality, but can also exploit the historical solutions, finding local best segments (called experience) to narrow the searching space smoothly, so that it can accelerate the convergence process. Comparisons of VM-MMACO and existing optimization strategies within a given iteration number are performed on the publicly available TSP instances from TSPLIB. The results demonstrates that VM-MMACO significantly outperforms other optimization strategies. Finally, according to the accumulative learning theory, the learning mechanism could be studied further to make a much more intelligent algorithm.