A new indicator-based many-objective ant colony optimizer for continuous search spaces View Full Text


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Article Info

DATE

2017-03

AUTHORS

Jesús Guillermo Falcón-Cardona, Carlos A. Coello Coello

ABSTRACT

In this paper, we propose a novel multi-objective ant colony optimizer (called iMOACOR) for continuous search spaces, which is based on ACOR and the R2 performance indicator. iMOACOR is the first multi-objective ant colony optimizer (MOACO) specifically designed to tackle continuous many-objective optimization problems (i.e., multi-objective optimization problems having four or more objectives). Our proposed iMOACOR is compared to three state-of-the-art multi-objective evolutionary algorithms (NSGA-III, MOEA/D and SMS-EMOA) and a MOACO algorithm called MOACOR using standard test problems and performance indicators taken from the specialized literature. Our experimental results indicate that iMOACOR is very competitive with respect to NSGA-III and MOEA/D and it is able to outperform SMS-EMOA and MOACOR in most of the test problems adopted. More... »

PAGES

71-100

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11721-017-0133-x

DOI

http://dx.doi.org/10.1007/s11721-017-0133-x

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