MH-MOEA: A New Multi-Objective Evolutionary Algorithm Based on the Maximin Fitness Function and the Hypervolume Indicator View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Adriana Menchaca-Mendez , Carlos A. Coello Coello

ABSTRACT

In this paper, we propose an approach that combines a modified version of the maximin fitness function and the hypervolume indicator for selecting individuals into a Multi-Objective Evolutionary Algorithm (MOEA). Our proposed selection mechanism is incorporated into a MOEA which adopts the crossover and mutation operators of the Nondominated Sorting Genetic Algorithm-II (NSGA-II), giving rise to the so-called “Maximin-Hypervolume Multi-Objective Evolutionary Algorithm (MH-MOEA)”. Our proposed MH-MOEA is validated using standard test problems taken from the specialized literature, using from three to six objectives. Our results are compared with respect to those produced by MC-MOEA (which is based on the maximin fitness function and a clustering technique), MOEA/D using Penalty Boundary Intersection (PBI), which is based on decomposition and iSMS-EMOA (which is based on the hypervolume indicator). Our preliminary results indicate that our proposed MH-MOEA is a good alternative to solve multi-objective optimization problems having both low dimensionality and high dimensionality in objective function space. More... »

PAGES

652-661

Book

TITLE

Parallel Problem Solving from Nature – PPSN XIII

ISBN

978-3-319-10761-5
978-3-319-10762-2

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-10762-2_64

DOI

http://dx.doi.org/10.1007/978-3-319-10762-2_64

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1004092131


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