In order to solve the uncertainty problem of blood supply chain network design, a multi-objective robust optimization design model of blood supply chain network was established. Firstly, for the blood supply chain network with five nodes, an optimization function considering safe stock, minimum cost and shortest storage time was established, and the ε-constraint, Pareto optimization and robust optimization method were used to deal with the established model, so that the multi-objective problem was transformed into a single objective robust problem. Secondly, by improving the original Whale Optimization Algorithm (WOA), the concept of crossover and mutation of the differential algorithm was introduced to WOA to enhance the search ability and improve the limitations, so as to obtain the Differential WOA (DWOA), which was used to solve the processed model. Finally, a numerical example verified that the shortage of the robust model is 76% less than that of the deterministic model when the test problems are the same. Therefore, the optimization robust model has more advantages in dealing with demand shortage. Compared with WOA, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), DWOA has shorter interruption time and lower cost.

In view of the uncertainty of chemotaxis step length and the lack of constancy of elimination diffusion probability in the optimization process of traditional bacterial foraging algorithm, in order to solve the problem of high-dimensional engineering optimization, an emotional bacteria foraging algorithm based on non-uniform elimination-diffusion probability was proposed. Firstly, in the chemotaxis step, the Gus distribution search mechanism was used to update the bacteria individual positions, so as to solve the problem of poor search ability and easy to fall into local optimum caused by bacteria swimming or flipping on each dimension in a random way. The emotion perception factor was introduced, and the sudden change of emotional intelligence was used to realize the adaptive chemotaxis step size, so as to avoid premature convergence of the algorithm. Secondly, in view of the probability constancy of bacterial individuals in the process of elimination-diffusion, the idea of using linear and non-linear probability distributions to replace the traditional constant distribution to realize non-uniform distribution was proposed. By introducing the random value of dynamic factor, the bacterial individuals in the undefined search space were limited, so as to save the calculation cost of the algorithm. Six benchmark functions were used in the test, and the test results show that: in the case of low calculation cost, except on Rosenbrock function, the proposed algorithm has low iteration times and good optimization quality on all functions, and the algorithm convergence comparison results show that the proposed algorithm has good convergence.