Chaotic ant swarm for the traveling salesman problem

Chaotic ant swarm (CAS) is an optimization algorithm based on swarm intelligence theory, which has been applied to find the global optimum solution in continuous search space. However, it is not able to solve the combinational optimization problem directly, e.g., the traveling salesman problem (TSP)...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Nonlinear dynamics Jg. 65; H. 3; S. 271 - 281
Hauptverfasser: Wei, Zhen, Ge, Fangzhen, Lu, Yang, Li, Lixiang, Yang, Yixian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.08.2011
Springer Nature B.V
Schlagworte:
Algorithms
Automotive Engineering
Chaos theory
Classical Mechanics
Computer simulation
Control
Crossovers
Dynamical Systems
Engineering
Heuristic
Mapping
Mathematical models
Mechanical Engineering
Operators
Optimization
Original Paper
Swarm intelligence
Traveling salesman problem
Vibration
TSP
Combinational optimization
Swarm intelligence
Chaotic ant swarm
Global search
ISSN:0924-090X, 1573-269X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Chaotic ant swarm (CAS) is an optimization algorithm based on swarm intelligence theory, which has been applied to find the global optimum solution in continuous search space. However, it is not able to solve the combinational optimization problem directly, e.g., the traveling salesman problem (TSP). To tackle this problem, we propose a new method to solve the traveling salesman problem based on chaotic ant swarm, CAS-TSP for short. The CAS-TSP is developed by introducing a mapping from continuous space to discrete space, reverse operator and crossover operator into the CAS. Computer simulations demonstrate that the CAS-TSP is capable of generating optimal solution to instances of the TSPLIB in almost all test problems of sizes up to 150. Also a comparative computational study shows that this CAS-TSP algorithm is an efficient tool for solving TSP and this heuristic is competitive also with other heuristics.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-010-9889-x