To deal with the problem of single-scale mutation, premature convergence and slow search speed, a clone selection algorithm(CSA) with directional multi-scale gaussian mutation is proposed. To implement the share of information between antibodies, the directional evolution mechanism is utilized to induce the antibodies to evolve to the best solution region. The special multi-scale Gaussian mutation operators are introduced to make antibodies explore the search space more efficiently. The large-scale mutation operators can be utilized to quickly localize the global optimized space at the early evolution, while the small-scale mutation operators can implement local accurate minima solution search at the late evolution, which can make the algorithm explore the global and local minima thoroughly at the same time. The comparison of the performance of the proposed approach with other CSAs with different mutations is experimented. The experimental results show that
the proposed method can not only effectively solve the premature convergence problem, but also significantly speed up the convergence and improve the stability.