Decomposition-Based Approach for Solving Large Scale Multi-objective Problems View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Luis Miguel Antonio , Carlos A. Coello Coello

ABSTRACT

Decomposition is a well-established mathematical programming technique for dealing with multi-objective optimization problems (MOPs), which has been found to be efficient and effective when coupled to evolutionary algorithms, as evidenced by MOEA/D. MOEA/D decomposes a MOP into several single-objective subproblems by means of well-defined scalarizing functions. It has been shown that MOEA/D is able to generate a set of evenly distributed solutions for different multi-objective benchmark functions with a relatively low number of decision variables (usually no more than 30). In this work, we study the effect of scalability in MOEA/D and show how its efficacy decreases as the number of decision variables of the MOP increases. Also, we investigate the improvements that MOEA/D can achieve when combined with coevolutionary techniques, giving rise to a novel MOEA which decomposes the MOP both in objective and in decision variables space. This new algorithm is capable of optimizing large scale MOPs and outperforms MOEA/D and GDE3 when solving problems with a large number of decision variables (from 200 up to 1200). More... »

PAGES

525-534

References to SciGraph publications

Book

TITLE

Parallel Problem Solving from Nature – PPSN XIV

ISBN

978-3-319-45822-9
978-3-319-45823-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-45823-6_49

DOI

http://dx.doi.org/10.1007/978-3-319-45823-6_49

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1017836429


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