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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on evolutionary computation Ročník 16; číslo 6; s. 861 - 875
Hlavní autori:	Lopez-Ibanez, M., Stutzle, T.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.12.2012 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	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.
Bibliografia:	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