The mutation and clone rates are big at the initial stage of the algorithm; AEB 071 so antibody with low affinity has the chance to clone and evolve, which helps to extend the search space. At the late stage of the algorithm, the mutation and clone rates are small; so antibody with big affinity is protected and global convergence rate is accelerated. Based on the aforementioned detailed analysis, C-ACSA approach can be designed as the following procedure. Step 1 . — Initialize the group of antibody. Generate N antibodies and constitute the species group P. Step 2 . — Count the affinities and sort antibodies according

to their affinities in an ascending order. Step 3 . — Clone each antibody in P and then get a new species group C. The number of clone is ni = wmax (1 − (i − 1)/N) and ni ≥ wmin , where i is the sequence of antibody after sorting. wmax is the maximum clone number, wmin is the minimum clone number, and means rounding. Step 4 . — Use mutation operation

to update each antibody in C. And get the new species group C′. The mutation rate is inversely proportional to evolution generation li = Qcloud(1 − l/L), where l is the current generation and L is the maximum generation. Step 5 . — Choose the first dl antibodies in C′ and replace the worst dl antibodies in P by them, dl=f–fmin⁡D/f¯, where D is the coefficient, f- is the average value of affinities in C′, and fmin is the minimum value of affinities in C′. Step 6 . — If current status does not meet the terminal condition (the maximum computing times), go to Step 2. Otherwise, go to Step 7. Step 7 . — Output the best solution, that is, the optimal location of freight centers. 5. Numerical Experiment In order to show the efficiency and effectiveness of the proposed model and approach, this section applies the model and C-ACSA to optimize the location of centers. In the programming area, there are 23 shippers and 7 candidate freight transport centers; distances between shippers and railway freight transport centers are shown in Table 1. The distances satisfy the triangle inequality. The distributions of transport demand are shown in Table

GSK-3 2, and the distributions are homogeneous distribution. The parameters of the optimal model are c = 0.1((million CNY)/(km−1·Mt−1)). μ1 = 0.6, μ2 = 0.4, p = 4, ε = 15, DC = 12, Capj = 40(Mt), and Cj = 100(million CNY). Table 1 Distances between shippers and candidate centers (km). Table 2 The distribution of transport demand (Mt). The parameters of the C-ACSA are N = 20, wmax = 8, wmin = 2, L = 100, D = 10, c1 = 60, and c2 = 10. Using C# to solve the experiment. 300 scenarios were simulated stochastically and the model was solved under three weights of κ which were 0, 10, and 20. When κ is 0, the robust model is expected optimization model. The result of location problem is shown in Table 3. The computing time is around 2s. Also, ILOG Cplex program is devised.