Ant colony optimization (ACO) algorithm is an effective method for solving combinatorial optimization problems. However, the ant colony algorithm has the tendency to fall into the local optimal solution, especially for the complex traveling salesman problems (TSP). Aiming at the disadvantages of ant colony algorithm, an ant colony algorithm based on multiple state transition operators (STOACO) is proposed in this paper. First, we select suitable cities from the unvisited cities as elements of the dynamic search table and the dynamic search table is established to enable the ants to find a good solution at the initial stage, improving the convergence of the algorithm. Then, in order to avoid the algorithm falling into the local optimum, three specific state transition operators are introduced to further strengthen global search ability. Finally, STOACO is compared with other ant colony algorithms, and the simulation results show that STOACO has better performance on the TSP.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.