Identification And Application Based On Biological Optimization Algorithm For Dual-rate System

Container Loading Problem (CLP) is the important link of the logistics distribution. It's plan has a great influence to the efficiency of logistics system and transportation cost. Container loading problem with multi-constraints is a complicated combinatorial optimization problem. Because it's a NP-hard problem, a algorithm with good performance should be designed.Ant colony optimization algorithm (ACO) is a new type of intelligent optimization algorithms, especially suitable for solving the complicated combinatorial optimization problems. This algorithm is widely used to solve the problem like the traveler problem and vehicle scheduling problem and obtain good effect. Therefore, ant colony optimization algorithm is used to solve CLP.Multi-population binary ant colony optimization algorithm which is based on the distribution of food quantity (FMPBACO) is designed. The ants quantity and death of population is decided by the food quantity. Population learns from each other by the means of pheromones mixed. The complexity of the algorithm is analyzed. FMPBACO is applied in the 0/1 multi-knapsack problem, and through solving the test set of SAC-94 Suite show the performance of the algorithm.A trifurcate tree of space is designed according to the trait of CLP and available space is divided by the strategy of trifurcate tree. Aiming at the weakly heterogeneous container loading problem, FMPBACO combining heuristic rules is designed. First, using FMPBACO determine the set of preparatory loading goods. Then, using heuristics determine the priority of the goods.The complexity of the algorithm is analyzed. Through testing two examples, the space utilization is high by using this algorithm.Aiming at the strongly heterogeneous container loading problem, mixed ant colony algorithm is designed. The solution of problem is divided in two parts, the priority of the goods and the goods'state. Based on heuristic rules, the larger goods has priority to pack in container, so volume is considered as heuristic information. The sequence that ant searches crosses with historical optimal sequence. The optimal one among the three sequences is choosed as the sequence that ant searches. In order to avoid pheromone over-rapid saturated, pheromone is updated by adopting two volatile coefficient. The complexity of the algorithm is analyzed. Through testing three examples, the space utilization is high by using this algorithm.