Abstract
As a swarm intelligence optimization algorithm, ant colony algorithm (ACO) has a good application in combinatorial optimization problems, in which traveling salesman problem (TSP) is an important application of ACO algorithm. It shows the powerful ability of ant colony algorithm to find short paths through graphics. However, there are obvious defects in the ant colony algorithm. When the scale of the ant colony is large, the convergence time of the algorithm becomes longer and the local optimal state is easy to fall into. In this paper, a dynamic pheromone ant colony optimization algorithm based on CW saving algorithm is proposed. Initially, a general path range is found by CW saving value algorithm, and the pheromone matrix can be reasonably configured, so that the ant colony algorithm can quickly get a better solution in the initial optimization. At the same time, the optimization scheme can be adjusted in real time according to the situation of path optimization. Large ant colony searches for other paths. Combined with 3-opt local search algorithm, the ant colony can find the optimal path more quickly. The experimental results show that the improved ant colony algorithm has better convergence speed and solution quality than other ant colony algorithms.
Highlights
With the rapid development of computer technology, the application field of intelligent computing methods is more and more extensive
This paper proposes a dynamic ant colony optimization algorithm based on CW saving algorithm
As a famous NP hard problem, there is no exact method to find the exact solution of traveling salesman problem (TSP) problem
Summary
With the rapid development of computer technology, the application field of intelligent computing methods is more and more extensive. Heuristic optimization algorithm is an algorithm that imitates biological behaviour to solve problems inspired by the laws of nature. When dealing with big data problems, the calculation time is long, and it is easy to fall into local optimal solution, even causing stagnation. In response to these problems, many scholars have put forward corresponding improvement methods [5]. The core technology of ant colony algorithm lies in the choice of pheromone update mode and state transition probability. In document [14], Chen W proposes to combine particle swarm optimization with ant colony algorithm to achieve win-win results in search time and performance
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