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Hierarchy Ranking Method for Multimodal Multiobjective Optimization With Local Pareto Fronts

Multimodal multiobjective problems (MMOPs) commonly arise in real-world situations where distant solutions in decision space share a very similar objective value. Traditional multimodal multiobjective evolutionary algorithms (MMEAs) prefer to pursue multiple Pareto solutions that have the same objective values. However, a more practical situation in engineering problems is that one solution is slightly worse than another in terms of objective values, while the solutions are far away in the decision space. In other words, such problems have global and local Pareto fronts (PFs). In this study, we proposed several benchmark problems with several local PFs. Then, we proposed an evolutionary algorithm with a hierarchy ranking method (HREA) to find both the global and the local PFs based on the decision maker’s preference. Regarding HREA, we proposed a local convergence quality evaluation method to better maintain diversity in the decision space. Moreover, a hierarchy ranking method was introduced to update the convergence archive. The experimental results show that HREA is competitive compared with other state-of-the-art MMEAs for solving the chosen benchmark problems.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.27 No.1 February 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 98-110

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

EVOLUTIONARY COMPUTATION
MULTIMODAL MULTIOBJECTIVE OPTIMIZATION
DIVERSITY-PRESERVING MECHANISMS
LOCAL PARETO FRONT (PF)

Info Detail Spesifik

10.1109/TEVC.2022.3155757

Pernyataan Tanggungjawab

Wenhua Li, Xingyi Yao, Tao Zhang, Rui Wang, Ling Wang

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