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A Multipopulation Evolutionary Algorithm for Solving Large-Scale Multimodal Multiobjective Optimization Problems

Multimodal multiobjective optimization problems (MMOPs) widely exist in real-world applications, which have multiple equivalent Pareto-optimal solutions that are similar in the objective space but totally different in the decision space. While some evolutionary algorithms (EAs) have been developed to find the equivalent Pareto-optimal solutions in recent years, they are ineffective to handle large-scale MMOPs having a large number of variables. This article thus proposes an EA for solving large-scale MMOPs with sparse Pareto-optimal solutions, i.e., most variables in the optimal solutions are 0. The proposed algorithm explores different regions of the decision space via multiple subpopulations and guides the search behavior of the subpopulations via adaptively updated guiding vectors. The guiding vector for each subpopulation not only provides efficient convergence in the huge search space but also differentiates its search direction from others to handle the multimodality. While most existing EAs solve MMOPs with 2-7 decision variables, the proposed algorithm is shown to be effective for benchmark MMOPs with up to 500 decision variables. Moreover, the proposed algorithm also produces a better result than state-of-the-art methods for the neural architecture search.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.25 No.3 June 2021

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 405 - 418

Bahasa

English

ISBN/ISSN

-

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

-

Edisi

-

Subjek

EVOLUTIONARY ALGORITHM (EA)
LARGE-SCALE OPTIMIZATION
MULTIMODAL MULTIOBJECTIVE OPTIMIZATION
NEURAL ARCHITECTURE SEARCH
SPARSE PARETO-OPTIMAL SOLUTIONS

Info Detail Spesifik

DOI: 10.1109/TEVC.2020.3044711

Pernyataan Tanggungjawab

Ye Tian, Ruchen Liu, Xingyi Zhang, Haiping Ma, Kay Chen Tan, Yaochu Jin

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