Application Study On The Optimization Of Military Logistics Distribution Based On Ant Colony Algorithm

With the development of computer technology, some new intellective heuristic algorithms are developed quickly and applied broadly.The ant colony algorithm (ACA) is a kind of random search method that developed by coping the natural ant behavior, because of the characters of the characters of running impliedly side by side and searching overall, it makes ACA have advantage that other routine optimization algorithms can't own. However, ACA is a bionic algorithm developed in recent years. It remains to be perfect in theory or the implementation methods, only this, We can give play to the performance and characteristic of ACA better and make it widely used.Ammunition sampling is a special conveyance problem with unfull loading, transportation with one vehicle, much more sampling point, constituted a special problem of TSP , called "mimetic traveling salesman problem"(m-TSP). Proceeds with transportation of the special goods in military logistics at first, the concepts of logistics are shown and the statistics and trend of logistics in our country is analyzed, some of the important problems about designing and implementing of logistics has been proposed. Then put forward the question, provide the mathematics describe, set up the math model. This paper has probed into the modern bionic optimized algorithms put forward in the 1990s —ACA and the max-min ACS(MMACS)which put forward later in order to overcome the shortcoming of ACA. It's slowly to solve the massive problem and difficult to find the contentment result with ACS .For this,the paper tackle traveling salesman problem(TSP) with characteristic of clustering, the TSP problem is divided into several sub-problems by clustering processing, and then all the sub-problems will be solved in parallelization. The result shows: this optimization algorithm accords with the practical problem, and it turns qualitative analysis to ration, the strong maneuverability and the convenient enlargement. It avoids the blindness to due with this problem primitively. It has stronger application reference value and practice directive significance.