Abstract
Ant colony algorithm has been widely applied to lots of fields, such as combinatorial optimization, function optimization, system identification, network routing, robot path planning, data mining and large-scale integrated circuit design of integrated wiring, etc. And it achieved good results. But it still has one weak point which is the slowing convergence speed. To aim at the lacks, an improved ACO is presented. This paper studies a kind of improved ant colony algorithm with crossover operator which makes crossover operator among better results at the end of each iteration. The experiment results indicate that the improved ACO is effectual.
Highlights
Research over the recent years has shown that the ant colony optimization (ACO) has a powerful capacity to find out solutions to combinatorial optimization problems, has the advantage of distributed computing, and is easy to accommodate with other algorithms, displaying powerful robustness
A two-stage ant colony optimization (ACO) algorithm is introduced and the results show that ACO outperformed the other algorithms and reached better solutions in a faster computational time
On the basis of the solution to the Traveling Salesman Problem (TSP), we studied a kind of improved ant colony algorithm with crossover operator (COACO)
Summary
Research over the recent years has shown that the ACO has a powerful capacity to find out solutions to combinatorial optimization problems, has the advantage of distributed computing, and is easy to accommodate with other algorithms, displaying powerful robustness. The deficiency remains to the bottleneck constraining the ACO from being widely applied in large-scale optimization problems For this end, on the basis of the solution to the Traveling Salesman Problem (TSP), we studied a kind of improved ant colony algorithm with crossover operator (COACO). Other batches of ants will use the pheromone left by the first batch of ants as a kind of reference information and select the desired path according to the pheromone density For those ants that carry the food back to their colony, when they head for the food source once again, they will not travel along the same route but will reselect a new path. The shortest path in NC loops will be the shortest path found out by the algorithm
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