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

Qinqin Fan and Xuefeng Yan ()
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Qinqin Fan: East China University of Science and Technology
Xuefeng Yan: East China University of Science and Technology

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 1, No 3, 35-49

Abstract: 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.

Keywords: Differential evolution; Multi-objective optimization; $$p$$ p -Xylene oxidation reaction process; Evolutionary algorithms; Pareto dominance (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10845-015-1087-8

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