蟻群算法在車輛路徑優(yōu)化中的應用畢業(yè)設(shè)計論文

1、本科畢業(yè)生設(shè)計（論文）1畢 業(yè) 設(shè) 計（論 計（論 文） 文）題目：蟻群算法在車輛路徑優(yōu)化中的應用 題目：蟻群算法在車輛路徑優(yōu)化中的應用姓 名 夏彬彬 學 號 0910312134 所在學院 所在學院 湖北工業(yè)大學 專業(yè)班級 專業(yè)班級 09 計職 1 班 指導教師 指導教師 宗欣露 日 期

2、 2013 2013 年 5 月 8 日 本科畢業(yè)生設(shè)計（論文）3ABSTRACTMany practical engineering problems can be abstracted as corresponding combinatorial optimization problem, TSP problem is an example of all as a combinatorial optimization pr

3、oblem, it has become and will continue to be a new combinatorial optimization algorithm of standard test problems. In theory, using the exhaustion method can solve the TSP problem optimal solution; But for the existing c

4、omputer, let it in such a large search space to seek the optimal solution, it is almost impossible. So, all kinds of algorithm arises at the historic moment, the approximate solution of the TSP problem described in this

5、paper, ant colony algorithm (AC) is among them. Has appeared a lot of heuristic algorithm and ant colony algorithm as a kind of new heuristic algorithm, has been successfully used in solving TSP problems. Ant secretion b

6、y pheromones to strengthen the good path pheromone concentration, at the same time according to the path to choose the next path pheromone concentration: good paths will be more and more ants to choose, so that more info

7、rmation will cover good path; Eventually all the ants on a good path. This positive feedback based on the pheromone of ant principle is the key to the whole algorithm. This paper introduces the basic concept of ant colon

8、y algorithm, principle and characteristics of ant colony algorithm, according to the disadvantages of ant colony algorithm optimization. Adopting roulette selection instead of the basic framework by heuristic function an

9、d choose path pheromone, pheromone passing parameters of improved ant colony algorithm, make the whole algorithm find the optimal solution more quickly. Second, limiting the maximum and the minimum maximum minimum optimi