Parallelized genetic ant colony systems for solving the traveling salesman problem

2011 ◽  
Vol 38 (4) ◽  
pp. 3873-3883 ◽  
Author(s):  
Shyi-Ming Chen ◽  
Chih-Yao Chien
Keyword(s):  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony Systems ◽  
The Traveling Salesman Problem

Improved Ant Colony Algorithm for Optimal Solution TSP

2013 ◽  
Vol 765-767 ◽  
pp. 699-702
Author(s):  
Tian Yuan Zhou
Keyword(s):  
Traveling Salesman Problem ◽  
Ant Colony Algorithm ◽  
Search Strategy ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Algorithm Analysis ◽  
Simulation Testing ◽  
The Traveling Salesman Problem ◽  
Improved Ant Colony Algorithm

Based on the ant colony algorithm analysis and research, this paper proposed an improved ant colony algorithm. Through updating pheromone and optimal search strategy, then applied to the Traveling Salesman Problem (TSP), effectively improved the searching capability of the algorithm. Finally through the simulation testing and analysis, verified that the improved ant colony algorithm is effective, and has good performance.

scholarly journals Influence of parameters of the ant colony algorithm on the traveling salesman problem solution

2014 ◽  
Vol 4 (4(70)) ◽  
pp. 18
Author(s):  
Ігор Андрійович Могила ◽  
Ірина Іванівна Лобач ◽  
Оксана Андріївна Якимець
Keyword(s):  
Traveling Salesman Problem ◽  
Ant Colony Algorithm ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Problem Solution ◽  
The Traveling Salesman Problem

Intelligent Ant Colony System for Traveling Salesman Problem and Clustering

2011 ◽  
pp. 18-42
Author(s):  
Shu-Chuan Chu ◽  
Jeng-Shyang Pan
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Data Clustering ◽  
Heuristic Algorithms ◽  
Computation Time ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
K Nearest Neighbor ◽  
Ant Colony Systems

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

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