A variable scale ant colony algorithm to the optimization of beer recipe

The optimization of beer recipe is a powerful approach to improve the efficiency of beer company. But for recipe optimization problems, the traditional mathematical optimization methods achieve more complex and lack the robust search for the optimal solution. Ant colony algorithm (ACA) is fit to solve the combinatorial optimization problems, but it has disadvantage of slow convergence and time-consuming in the process of evolution. Therefore, the variable scale ant colony algorithm was presented and the scope of search is shorted constantly in the iterative process to improve the efficiency of optimization in the paper. Then the study of new ACA of the optimization of beer recipe is also presented, which meet production targets, and achieve the lowest total cost of the raw materials. Simulation results show that comparing with the traditional ACA, the improved ACA has more global search capability and better robustness, and it also has practical value because of its easy implementation.