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

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  • Qinqin Fan

    (East China University of Science and Technology)

  • Xuefeng Yan

    (East China University of Science and Technology)

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.

Suggested Citation

  • Qinqin Fan & Xuefeng Yan, 2018. "Multi-objective modified differential evolution algorithm with archive-base mutation for solving multi-objective $$p$$ p -xylene oxidation process," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 35-49, January.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:1:d:10.1007_s10845-015-1087-8
    DOI: 10.1007/s10845-015-1087-8
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    References listed on IDEAS

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    1. Ali, Musrrat. & Siarry, Patrick & Pant, Millie., 2012. "An efficient Differential Evolution based algorithm for solving multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 217(2), pages 404-416.
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    Cited by:

    1. Alejandro Alvarado-Iniesta & Luis Gonzalo Guillen-Anaya & Luis Alberto Rodríguez-Picón & Raul Ñeco-Caberta, 2020. "Multi-objective optimization of an engine mount design by means of memetic genetic programming and a local exploration approach," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 19-32, January.

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