Adaptive Update Range of Solutions in MOEA/D for Multi and Many-Objective Optimization View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Hiroyuki Sato

ABSTRACT

MOEA/D, a representative multi-objective evolutionary algorithm, decomposes a multi-objective optimization problem into a number of single objective optimization problems and tries to approximate Pareto front by simultaneously optimizing each of these single objective problems. MOEA/D has several options to calculate a scalar value from multiple objective function values of a solution. In many-objective optimization problems including four or more objective functions, MOEA/D using the weighted sum scalarizing function achieves high search performance. However, the weighted sum has a serious problem that the entire concave Pareto front cannot be approximated. To overcome this problem of the weighted sum based MOEA/D, in this work we propose a method to adaptively determine update ranges of solutions in the framework of MOEA/D. The experimental results show that the weighted sum based MOEA/D using the proposed solution update method can approximate the entire concave Pareto front and improve the search performance. More... »

PAGES

274-286

Book

TITLE

Simulated Evolution and Learning

ISBN

978-3-319-13562-5
978-3-319-13563-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-13563-2_24

DOI

http://dx.doi.org/10.1007/978-3-319-13563-2_24

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1016612283


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