Particle Swarm Optimization (PSO), as a group intelligence algorithm, effectively improves the practicability of the portfolio model, but it has the disadvantages of low search accuracy and easy to fall into local optimum. In order to overcome its shortcomings, this study proposes a particle swarm optimization algorithm based on the Beetle Antennae Search (Abbreviated as BAS), and applies it to the portfolio model with full cost. In the Optimization algorithm based on BAS (BSO), the update rule of each particle is derived from BAS. In each iteration, it has its own judgment on the environment space, and not only depends on the historical best solution in the PSO and the current global optimal solution of the particle individual, thereby reducing the number of iterations, improving search speed and accuracy. The empirical results show that the algorithm is more stable and effective.