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A Novel Evolutionary Algorithm for Dynamic Constrained Multiobjective Optimization Problems

To promote research on dynamic constrained multiobjective optimization, we first propose a group of generic test problems with challenging characteristics, including different modes of the true Pareto front (e.g., convexity-concavity and connectedness-disconnectedness) and the changing feasible region. Subsequently, motivated by the challenges presented by dynamism and constraints, we design a dynamic constrained multiobjective optimization algorithm with a nondominated solution selection operator, a mating selection strategy, a population selection operator, a change detection method, and a change response strategy. The designed nondominated solution selection operator can obtain a nondominated population with diversity when the environment changes. The mating selection strategy and population selection operator can adaptively handle infeasible solutions. If a change is detected, the proposed change response strategy reuses some portion of the old solutions in combination with randomly generated solutions to reinitialize the population, and a steady-state update method is designed to improve the retained previous solutions. The experimental results show that the proposed test problems can be used to clearly distinguish the performance of algorithms, and that the proposed algorithm is very competitive for solving dynamic constrained multiobjective optimization problems in comparison with state-of-the-art algorithms.

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Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.24 No.4 August 2020

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 792 - 806

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

CHANGE RESPONSE
TEST PROBLEMS
DYNAMIC CONSTRAINED MULTIOBJECTIVE OPTIMIZATION
POPULATION SELECTION

Info Detail Spesifik

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Pernyataan Tanggungjawab

Qingda Chen, Jinliang Ding, Shengxiang Yang, Tianyou Chai

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