Continuous Ant Colony Algorithm And Its Applications In The Formulation Of Raw Materials Optimization

Ant colony algorithm is a highly innovative element heuristic search algorithm inspired form foraging behavior of real ants proposed by Marco Dorigo etc. It was proposed after the genetic algorithm, simulated annealing algorithm, artificial neural network algorithm, tabu search algorithm. Experiments show that the ant colony algorithm has good solution ability, but the ant colony algorithm also has defects such as slow convergence rate and easy to fall into local optimal solution. The purpose of this paper is the improved research of the ant colony algorithm in continuous domain optimization problem, enhance the optimized performance of ant colony algorithm in continuous optimization problem.Contrapose the discrete nature of ant colony algorithm, the difficulty of the ant colony optimization algorithm for solving continuous optimization problems is the representation, distribution and retention methods of the pheromone. In this paper, Gaussian probability density function was adopted as the pheromone model of the continuous optimization space, proposed the continuous ant colony optimization algorithm introduced memory table -TACO. Under the guidance of the pheromone, the global optimal ant executes local excavating search, through sample the pheromone model, other ants execute global exploratory search. All of the ants release certain pheromone in the traversed places to achieve the overall information sharing. On the basis of algorithm TACO, proposed constrained continuous ant colony optimization-CTACO for solve constrained optimization problem. Algorithm CTACO was combined advanced constraint processing mechanism and different global metastasis rules according the situation of feasible solution and infeasible solution. Simulation results show that the algorithm has good performance in dealing with the off-line performance, online performance, stability, robustness and convergence.Finally, constrained continuous ant colony optimization algorithm solves the raw materials recipe optimum design problem of beer in the practical engineering. Experimental results show that, compared with the business formula, the algorithm optimization scheme can significantly reduce the total cost of raw materials, improve production efficiency and economic benefits.