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...

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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
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