article

Multi-objective modified differential evolution algorithm with archive-base mutation for solving multi-objective $$p$$p-xylene oxidation process

Authors:

Xuefeng YanAuthors Info & Claims

Journal of Intelligent Manufacturing, Volume 29, Issue 1

Pages 35 - 49

https://doi.org/10.1007/s10845-015-1087-8

Published: 01 January 2018 Publication History

Abstract

Maximizing the diversity of the obtained objective vectors and increasing the convergence speed to the true Pareto front are two important issues in the design of multi-objective evolutionary algorithms (MOEAs). To solve complex multi-objective optimization problems (MOPs), a multi-objective modified differential evolution algorithm with archive-base mutation (MOMDE-AM) is proposed. In MOMDE-AM, with the purpose of reducing the loss of population evolution information, a modified mutation strategy with archive is introduced, which could utilize several useful inferior solutions and provide promising direction information toward the true Pareto front. The performance of MOMDE-AM is compared with five other MOEAs on five bi-objective and five tri-objective optimization problems. The simulation and statistical analysis results indicate that the overall performance of MOMDE-AM is better than those of the compared algorithms on these test functions. Finally, MOMDE-AM is used to optimize ten operation conditions of the $$p$$p-xylene oxidation reaction process; the results show that MOMDE-AM is an effective and efficient optimization tool for solving actual MOPs.

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Altiok MGündüz M(2024)MOAAA/D: a decomposition-based novel algorithm and a structural design applicationNeural Computing and Applications10.1007/s00521-024-09746-336:28(17345-17374)Online publication date: 1-Oct-2024
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Published In

cover image Journal of Intelligent Manufacturing

Journal of Intelligent Manufacturing Volume 29, Issue 1

January 2018

251 pages

ISSN:0956-5515

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Springer-Verlag

Berlin, Heidelberg

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Published: 01 January 2018

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Cited By

Altiok MGündüz M(2024)MOAAA/D: a decomposition-based novel algorithm and a structural design applicationNeural Computing and Applications10.1007/s00521-024-09746-336:28(17345-17374)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s00521-024-09746-3
Dubey GSingh HMaurya RSheoran KDhand G(2024)A hybrid forecasting system using convolutional-based extreme learning with extended elephant herd optimization for time-series predictionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09499-628:11-12(7093-7124)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00500-023-09499-6
Gupta MKumar NGupta NZaguia A(2022)Fusion of multi-modality biomedical images using deep neural networksSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07047-226:16(8025-8036)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s00500-022-07047-2

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