Result: Multi-objective optimisation by co-operative co-evolution

Title:
Multi-objective optimisation by co-operative co-evolution
Authors:
MANEERATANA, Kuntinee, BOONLONG, Kittipong, CHAIYARATANA, Nachol
Source:
PPSN VIII : parallel problem solving from nature (Birmingham, 18-22 September 2004)Lecture notes in computer science. :772-781
Publisher Information:
Berlin: Springer, 2004.
Publication Year:
2004
Physical Description:
print, 17 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, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Analyse non convexe, Non convex analysis, Análisis no convexo, Biomimétique, Biomimetics, Couverture, Coverage, Cobertura, Loi Pareto, Pareto distribution, Ley Pareto, Optimum Pareto, Pareto optimum, Optimo Pareto, Parallélisme, Parallelism, Paralelismo, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo, Résolution problème, Problem solving, Resolución problema, Topologie, Topology, Topología, Triage, Sorting, Tría, Uniformité, Uniformity, Uniformidad
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Mechanical Engineering, Chulalongkorn University, Phaya Thai Road, Pathum Wan, Bangkok 10330, Thailand
Research and Development Center for Intelligent Systems, King Mongkut's Institute of Technology North Bangkok, 1518 Piboolsongkram Road, Bangsue, Bangkok 10800, Thailand
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=16176914
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
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.16176914
Database:
PASCAL Archive

Further Information

This paper presents the integration between a co-operative co-evolutionary genetic algorithm (CCGA) and four evolutionary multi-objective optimisation algorithms (EMOAs): a multi-objective genetic algorithm (MOGA), a niched Pareto genetic algorithm (NPGA), a non-dominated sorting genetic algorithm (NSGA) and a controlled elitist non-dominated sorting genetic algorithm (CNSGA). The resulting algorithms can be referred to as co-operative co-evolutionary multi-objective optimisation algorithms or CCMOAs. The CCMOAs are benchmarked against the EMOAs in seven test problems. The first six problems cover different characteristics of multi-objective optimisation problems, namely convex Pareto front, non-convex Pareto front, discrete Pareto front, multi-modality, deceptive Pareto front and non-uniformity of solution distribution. In contrast, the last problem is a two-objective real-world problem, which is generally referred to as the continuum topology design. The results indicate that the CCMOAs are superior to the EMOAs in terms of the solution set coverage, the average distance from the non-dominated solutions to the true Pareto front, the distribution of the non-dominated solutions and the extent of the front described by the non-dominated solutions.