Tackling the Bi-criteria Facet of Multiple Traveling Salesman Problem with Ant Colony Systems

Author(s):  
Raluca Necula ◽  
Mihaela Breaban ◽  
Madalina Raschip
2009 ◽  
Vol 626-627 ◽  
pp. 717-722 ◽  
Author(s):  
Hong Kui Feng ◽  
Jin Song Bao ◽  
Jin Ye

A lot of practical problem, such as the scheduling of jobs on multiple parallel production lines and the scheduling of multiple vehicles transporting goods in logistics, can be modeled as the multiple traveling salesman problem (MTSP). Due to the combinatorial complexity of the MTSP, it is necessary to use heuristics to solve the problem, and a discrete particle swarm optimization (DPSO) algorithm is employed in this paper. Particle swarm optimization (PSO) in the continuous space has obtained great success in resolving some minimization problems. But when applying PSO for the MTSP, a difficulty rises, which is to find a suitable mapping between sequence and continuous position of particles in particle swarm optimization. For overcoming this difficulty, PSO is combined with ant colony optimization (ACO), and the mapping between sequence and continuous position of particles is established. To verify the efficiency of the DPSO algorithm, it is used to solve the MTSP and its performance is compared with the ACO and some traditional DPSO algorithms. The computational results show that the proposed DPSO algorithm is efficient.


Author(s):  
Shu-Chuan Chu ◽  
Jeng-Shyang Pan

Processes that simulate natural phenomena have successfully been applied to a number of problems for which no simple mathematical solution is known or is practicable. Such meta-heuristic algorithms include genetic algorithms, particle swarm optimization and ant colony systems and have received increasing attention in recent years. This work parallelizes the ant colony systems and introduces the communication strategies so as to reduce the computation time and reach the better solution for traveling salesman problem. We also extend ant colony systems and discuss a novel data clustering process using Constrained Ant Colony Optimization (CACO). The CACO algorithm extends the ant colony optimization algorithm by accommodating a quadratic distance metric, the Sum of K Nearest Neighbor Distances (SKNND) metric, constrained addition of pheromone and a shrinking range strategy to improve data clustering. We show that the CACO algorithm can resolve the problems of clusters with arbitrary shapes, clusters with outliers and bridges between clusters


Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 153 ◽  
Author(s):  
Anugrah K. Pamosoaji ◽  
Djoko Budiyanto Setyohadi

In this paper, a novel graph model to figure Collision-Free Multiple Traveling Salesman Problem (CFMTSP) is proposed. In this problem, a group of vehicles start from different nodes in an undirected graph and must visit each node in the graph, following the well-known Traveling Salesman Problem (TSP) fashion without any collision. This paper’s main objective is to obtain free-collision routes for each vehicle while minimizing the traveling time of the slowest vehicle. This problem can be approached by applying speed to each vehicle, and a novel augmented graph model can perform it. This approach accommodates not only the position of nodes and inter-node distances, but also the speed of all the vehicles is proposed. The proposed augmented graph should be able to be used to perform optimal trajectories, i.e., routes and speeds, for all vehicles. An ant colony optimization (ACO) algorithm is used on the proposed augmented graph. Simulations show that the algorithm can satisfy the main objective. Considered factors, such as limitation of the mission successfulness, i.e., the inter-vehicle arrival time on a node, the number of vehicles, and the numbers of vehicles and edges of the graph are also discussed.


Sign in / Sign up

Export Citation Format

Share Document