The Automatic Design of Multiobjective Ant Colony Optimization Algorithms

Multiobjective optimization problems are problems with several, typically conflicting, criteria for evaluating solutions. Without any a priori preference information, the Pareto optimality principle establishes a partial order among solutions, and the output of the algorithm becomes a set of nondomi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 16; H. 6; S. 861 - 875
Hauptverfasser: Lopez-Ibanez, M., Stutzle, T.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.12.2012
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithm design and analysis
Algorithmics. Computability. Computer arithmetics
Algorithms
Ant colony optimization
Ant colony optimization (ACO)
Applied sciences
automatic algorithm configuration
Computer science; control theory; systems
Context
Dealing
Decision theory. Utility theory
Equivalence
Exact sciences and technology
Flexibility
Formulations
Logistics
Mathematical models
multiobjective optimization
Operational research and scientific management
Operational research. Management science
Operations research
Optimization
Software
Software algorithms
Software engineering
Studies
Theoretical computing
traveling salesman problem
Traveling salesman problems
Vectors
automatic algorithm configuration
Ant colony optimization
Artificial life
Nondominated set
traveling salesman problem
Pareto optimum
Multiobjective programming
Algorithmics
Travelling salesman problem
Partial ordering
Flexibility
Particle swarm optimization
multiobjective optimization
Experimental result
Experimental design
Preference
A priori estimation
Problem solving
Swarm intelligence
Optimality principle
Ant colony optimization (ACO)
Mathematical programming
ISSN:1089-778X, 1941-0026
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Multiobjective optimization problems are problems with several, typically conflicting, criteria for evaluating solutions. Without any a priori preference information, the Pareto optimality principle establishes a partial order among solutions, and the output of the algorithm becomes a set of nondominated solutions rather than a single one. Various ant colony optimization (ACO) algorithms have been proposed in recent years for solving such problems. These multiobjective ACO (MOACO) algorithms exhibit different design choices for dealing with the particularities of the multiobjective context. This paper proposes a formulation of algorithmic components that suffices to describe most MOACO algorithms proposed so far. This formulation also shows that existing MOACO algorithms often share equivalent design choices, but they are described in different terms. Moreover, this formulation is synthesized into a flexible algorithmic framework, from which not only existing MOACO algorithms may be instantiated, but also combinations of components that were never studied in the literature. In this sense, this paper goes beyond proposing a new MOACO algorithm, but it rather introduces a family of MOACO algorithms. The flexibility of the proposed MOACO framework facilitates the application of automatic algorithm configuration techniques. The experimental results presented in this paper show that the automatically configured MOACO framework outperforms the MOACO algorithms that inspired the framework itself. This paper is also among the first to apply automatic algorithm configuration techniques to multiobjective algorithms.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2011.2182651