An Ant Colony Optimization and Bayesian Network Structure Application for the Asymmetric Traveling Salesman Problem

2012 ◽  
pp. 74-78 ◽  
Author(s):  
Nai-Hua Chen
Keyword(s):  
Bayesian Network ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Network Structure ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Asymmetric Traveling Salesman Problem ◽  
Bayesian Network Structure

scholarly journals A model induced max-min ant colony optimization for asymmetric traveling salesman problem

2013 ◽  
Vol 13 (3) ◽  
pp. 1365-1375 ◽  
Author(s):  
Jie Bai ◽  
Gen-Ke Yang ◽  
Yu-Wang Chen ◽  
Li-Sheng Hu ◽  
Chang-Chun Pan
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Asymmetric Traveling Salesman Problem

An Improved Ant Colony System Based on Negative Biased

2010 ◽  
Vol 439-440 ◽  
pp. 558-562
Author(s):  
Jin Qiu Yang ◽  
Jian Gang Yang ◽  
Gen Lang Chen
Keyword(s):  
Convergence Rate ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony System ◽  
Ant System

Ant System (AS) was the first Ant Colony Optimization (ACO) algorithm, which converged too slowly and consumed huge computation. Among the variants of AS, Ant Colony System (ACS) was one of the most successful algorithms. But ACS converged so rapidly that it always was in early stagnation. An improved Ant Colony System based on Negative Biased (NBACS) was introduced in the paper to overcome the early stagnation of the ACS. Experiments for Traveling Salesman Problem (TSP) showed that better solutions were obtained at the same time when the convergence rate accelerated more rapidly.

Particle Swarm Optimization Combined with Ant Colony Optimization for the Multiple Traveling Salesman Problem

2009 ◽  
Vol 626-627 ◽  
pp. 717-722 ◽  
Author(s):  
Hong Kui Feng ◽  
Jin Song Bao ◽  
Jin Ye
Keyword(s):  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Particle Swarm ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Great Success ◽  
Swarm Optimization ◽  
Continuous Space ◽  
Multiple Traveling Salesman Problem

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.

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