Result: Multiobjective optimization based on coevolutionary algorithm
Title:
Multiobjective optimization based on coevolutionary algorithm
Authors:
Source:
RSCTC 2004 : rough sets and current trends in computing (Uppsala, 1-5 June 2004)Lecture notes in computer science. :774-779
Publisher Information:
Berlin: Springer, 2004.
Publication Year:
2004
Physical Description:
print, 4 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Coévolution, Coevolution, Coevolución, Diversité, Diversity, Diversidad, Optimisation, Optimization, Optimización, Optimum Pareto, Pareto optimum, Optimo Pareto, Programmation mathématique, Mathematical programming, Programación matemática, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo, Taux convergence, Convergence rate, Relación convergencia
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Institute of Intelligent Information Processing, Xidian University, Xi'an, 710071, China
ISSN:
0302-9743
Rights:
Copyright 2004 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
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
Accession Number:
edscal.15851891
Database:
PASCAL Archive
Further Information
With the intrinsic properties of multiobjective optimization problems in mind, multiobjective coevolutionary algorithm (MOCEA) is proposed. In MOCEA, a Pareto crossover operator, and 3 coevolutionary operators are designed for maintaining the population diversity and increasing the convergence rate. Moreover, a crowding distance is designed to reduce the size of the nondominated set. Experimental results demonstrate that MOCEA can find better solutions at a low computational cost. At the same time, the solutions found by MOCEA scatter uniformly over the entire Pareto front.