Name
Abstract | ||
|---|---|---|
Ant colony optimization (ACO) has been successfully applied to solve combinatorial optimization problems, but it still has some drawbacks such as stagnation behavior, long computational time, and premature convergence. These drawbacks will be more evident when the problem size increases. In this paper, we reported the analysis of using a lower pheromone trail bound and a dynamic updating rule for the heuristic parameters based on entropy to improve the efficiency of ACO in solving Traveling Salesman Problems (TSPs). TSPs are NP-hard problem. Even though the problem itself is simple, when the number of city is large, the search space will become extremely large and it becomes very difficult to find the optimal solution in a short time. From our experiments, it can be found that the proposed algorithm indeed has superior search performance over traditional ACO algorithms do. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.20965/jaciii.2007.p0433 | JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS |
Keywords | Field | DocType |
ant colony optimization, traveling salesman problems, entropy | Ant colony optimization algorithms,Bottleneck traveling salesman problem,Mathematical optimization,Extremal optimization,Computer science,Travelling salesman problem,Artificial intelligence,2-opt,Machine learning,Metaheuristic | Journal |
Volume | Issue | ISSN |
11 | 4 | 1343-0130 |
Citations | PageRank | References |
12 | 0.92 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuo-Sheng Hung | 1 | 12 | 1.60 |
| Shun-Feng Su | 2 | 1194 | 97.62 |
| Zne-Jung Lee | 3 | 940 | 43.45 |