The algorithm is first proposed by Italian scholars M. Dorigo, V.maniez-Zo, A.Colorini et al., Called ant colony system (Ant ", the model has been successfully applied to the Travel Business Problem (TSP), two The combination of NP-difficult combination of NP-difficult problems, sorting problems can be comparable to universal heuristic algorithms such as analog annealing, genetic algorithm. Combination of ant colony algorithm and partial search algorithm (called mixed ant colony algorithm) The result is applied to the secondary assignment problem and sorting, and the result can be comparable to the special algorithm]. Ant colony system model gradually causing other researchers, D. Costa and A.hertz. In M. Based on the research results of Dorigo et al. Proposed a general model for solving the type of allocation type (AssignmentTyPeplem), and is used to study the color matching problem. G.Bilchev, ICPARMEE studies the ant colony system model for solving continuous space optimization. . The ant colony algorithm is a new heuristic algorithm to imitating ant working methods. Biological studies have shown that a group of ants can find the shortest path between food sources and nests, while single ants can not. Ants are collaborative The method is that they leave a certain amount of pixel (trace) on the road passed, the trace can be detected by other ants, the more traces on a road, the other ant will follow the path, thus the path The trace is enhanced. Ant algorithm pseudo code Begin initialization: t ← 0 ortho ← 0 (iTeration for iterative steps) Positioning M ants random at N top points; loop: Place all ants initial starting point in current Solution; for i ← 0 to n-1 do fork ← 1 to m do Press the probability to select the vertex j; mobile ant k to the vertex j; put the vertex j in ant K's current solution End for t ← T 1 end for Calculate the Limet function value of each ant, update the current ideal solution calculation of each solution T ← T 1 reset all ← 0 Iteration ← Iteration 1 If Iteration
