Ant Colony Algorithm---intelligent algorithm

Intelligent algorithm---ant colony algorithm 1 ant colony algorithm and its basic idea ant colony algorithm is an intelligent optimization algorithm, which solves complex problems by Ant colony optimization, and ACO has better superiority in discrete optimization problem.     The basic idea (take the traveling quotient problem as an example) set up a number of ants, split parallel search.     After each ant has completed a tour, it releases pheromone on the road, which is proportional to the quality of the solution. The choice of Ant path is based on the intensity of pheromone (the initial pheromone amount is set equal), and the distance between two points is considered, and the random local search strategy is adopted.     This makes the distance shorter, and the amount of pheromone on it is larger, and later the Ant chooses the edge.     Each ant can only take the legal route (after 1 times per city and only 1 times), to set a taboo table to control.     All the ants are searched once is the iteration, each iteration once on all sides do a pheromone update, the original Ant died, the new ant carries on a new round of search.     The update pheromone includes the evaporation of the original pheromone and the increase of pheromone on the path. To reach a predetermined number of iterations, or stagnation (all ants choose the same path, the solution no longer changes), then the algorithm ends with the current optimal solution as the optimal solution of the problem 2 Representation 2.1 parameter representation

2.2 Calculation formula

3 ACO algorithm steps

4 ACO algorithm solves the traveling quotient question 4.1 question raises

4.2 Parameter settings

4.3 Calculation results First, the corresponding parameters for each node are like 5 results.

According to the conclusion, once again there are ants starting from five points, the path traversed as shown in the following five tables

Conclusion: When the stop phenomenon occurs, the optimal solution is obtained, the algorithm ends, the optimal solution is: AEDCBA, the target function value is 9

5 The advantages and disadvantages of ACO aco strong robustness--a little modification can be applied to other problems; distributed computing-inherently parallel; easy to combine with other heuristic algorithms. The shortcomings of PSO generally require longer search time; it is prone to stagnation; only for discrete problems

Ant Colony Algorithm---intelligent algorithm