Traditional ant colony optimization (ACO) often suffers from insufficient initial pheromone information, premature convergence, and limited adaptability across problem scales when solving the traveling salesman problem (TSP). This study proposes a multi-strategy adaptive ant colony optimization algorithm, termed MSA-ACO. Unlike hybrid ACO methods whose added strategies are governed by separate or predetermined rules, MSA-ACO uses smoothed normalized pheromone entropy as a shared internal search-state signal. This signal coordinates search-stage switching, exploitation preference, pheromone evaporation intensity, heuristic-information dependence, and the adaptive 3-opt budget under scale-dependent control profiles. A rank-weighted multi-elite initialization method and candidate-restricted hierarchical 2-opt/3-opt search are further incorporated to improve early guidance and solution refinement. Experiments on 35 TSPLIB instances show that MSA-ACO attains the best-known solution on 22 instances and achieves an average best-solution deviation of 0.17%, compared with 3.87% for ACS and 5.27% for MMAS. Its maximum deviation is 1.22%, and the deviation remains within 1% on 33 instances. Nonparametric tests confirm significantly better overall solution quality than ACS and MMAS. Under equal distance-query budgets, MSA-ACO obtains the lowest mean tour length on st70, kroC100, and kroA150, with significant improvements over ACS-based local search on kroC100 and kroA150. Component-wise ablation and sequential timing results reveal instance-dependent component effects and identify 3-opt as a major source of computational cost as well as an important refinement mechanism on the larger instance. Moreover, compared with the fixed 1000-iteration setting, the scale-segmented iteration budget reduces average running time by 28.06–83.75% across four representative instances, while still attaining the BKS on eil76 and ts225 and yielding mean deviations of approximately 0.30% and 0.20% on ch150 and kroA200, respectively. Overall, MSA-ACO provides an effective scale-aware optimization framework for symmetric TSPs, combining consistently high solution quality with controlled computational effort across different instance scales.