• Runtime analysis of the 1-ANT...

Result: Runtime analysis of the 1-ANT ant colony optimizer

Title:
Runtime analysis of the 1-ANT ant colony optimizer
Authors:
DOERR, Benjamin, NEUMANN, Frank, SUDHOLT, Dirk, WITT, Carsten
Source:
Theoretical computer science. 412(17):1629-1644
Publisher Information:
Oxford: Elsevier, 2011.
Publication Year:
2011
Physical Description:
print, 26 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Optimisation et calcul variationnel numériques, Numerical methods in optimization and calculus of variations, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Divers, Miscellaneous, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Analyse algorithme, Algorithm analysis, Análisis algoritmo, Comportement, Behavior, Conducta, Heuristique, Heuristics, Heurística, Influence, Influencia, Informatique théorique, Computer theory, Informática teórica, Itération, Iteration, Iteracción, Méthode analyse, Analysis method, Método análisis, Méthode optimisation, Optimization method, Método optimización, Optimisation, Optimization, Optimización, Performance, Rendimiento, Preuve, Proof, Prueba, Robustesse, Robustness, Robustez, Résultat expérimental, Experimental result, Resultado experimental, 49XX, 65K10, 65Kxx, 68Q25, 68W40, 68Wxx, Ant colony optimization, Runtime analysis, Theory
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Max-Planck-Institut für Informatik, Saarbrücken, Germany
School of Computer Science, University of Adelaide, Adelaide, SA 5005, Australia
CERCIA, University of Birmingham, Birmingham, United Kingdom
DTU Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark
ISSN:
0304-3975
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=23942161
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Mathematics
Accession Number:
edscal.23942161
Database:
PASCAL Archive

Further Information

The runtime analysis of randomized search heuristics is a growing field where, in the last two decades, many rigorous results have been obtained. First runtime analyses of ant colony optimization (ACO) have been conducted only recently. In these studies simple ACO algorithms such as the 1-ANT are investigated. The influence of the evaporation factor in the pheromone update mechanism and the robustness of this parameter w.r.t. the runtime behavior have been determined for the example function ONEMAX. This work puts forward the rigorous runtime analysis of the 1-ANT on the example functions LEADINGONES and BINVAL. With respect to Evolutionary Algorithms (EAs), such analyses were essential to develop methods for the analysis on more complicated problems. The proof techniques required for the 1-ANT, unfortunately, differ significantly from those for EAs, which means that a new reservoir of methods has to be built up. Again, the influence of the evaporation factor is analyzed rigorously, and it is proved that its choice has a crucial impact on the runtime. Moreover, the analyses provide insight into the working principles of ACO algorithms. Our theoretical results are accompanied by experimental results that give us a more detailed impression of the 1-ANT's performance. Furthermore, the experiments also deal with the question whether using many ant solutions in one iteration can decrease the total runtime.