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On the Effect of the Cooperation of Indicator-Based Multiobjective Evolutionary Algorithms

For almost 20 years, quality indicators (QIs) have promoted the design of new selection mechanisms of multiobjective evolutionary algorithms (MOEAs). Each indicator-based MOEA (IB-MOEA) has specific search preferences related to its baseline QI, producing Pareto front approximations with different properties. In consequence, an IB-MOEA based on a single QI has a limited scope of multiobjective optimization problems (MOPs) in which it is expected to have a good performance. This issue is emphasized when the associated Pareto front geometries are highly irregular. In order to overcome these issues, we propose here an island-based multiindicator algorithm (IMIA) that takes advantage of the search biases of multiple IB-MOEAs through a cooperative scheme. Our experimental results show that the cooperation of multiple IB-MOEAs allows IMIA to perform more robustly (considering several QIs) than the panmictic versions of its baseline IB-MOEAs as well as several state-of-the-art MOEAs. Additionally, IMIA shows a Pareto-front-shape invariance property, which makes it a remarkable optimizer when tackling MOPs with complex Pareto front geometries.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.25 No.4 August 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 681 - 695

Bahasa

English

ISBN/ISSN

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Klasifikasi

-

Tipe Isi

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Tipe Media

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Tipe Pembawa

-

Edisi

-

Subjek

MULTIOBJECTIVE OPTIMIZATION
ISLAND MODEL
QUALITY INDICATORS (QIS)
SELECTION MECHANISM

Info Detail Spesifik

DOI: 10.1109/TEVC.2021.3061545

Pernyataan Tanggungjawab

Jesus Guillermo Falcon-Cardona, Hisao Ishibuchi, Carlos A. Coello Coello, Michael Emmerich

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