Optimization of reactor network design problem using jumping gene adaptation of differential evolution

Ashish M. Gujarathi, S. Purohit, B. Srikanth

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Detailed working principle of jumping gene adaptation of differential evolution (DE-JGa) is presented. The performance of the DE-JGa algorithm is compared with the performance of differential evolution (DE) and modified DE (MDE) by applying these algorithms on industrial problems. In this study Reactor network design (RND) problem is solved using DE, MDE, and DE-JGa algorithms: These industrial processes are highly nonlinear and complex with reference to optimal operating conditions with many equality and inequality constraints. Extensive computational comparisons have been made for all the chemical engineering problems considered. The results obtained in the present study show that DE-JGa algorithm outperforms the other algorithms (DE and MDE). Several comparisons are made among the algorithms with regard to the number of function evaluations (NFE)/CPU- time required to find the global optimum. The standard deviation and the variance values obtained using DE-JGa, DE and MDE algorithms also show that the DE-JGa algorithm gives consistent set of results for the majority of the test problems and the industrial real world problems.

Original languageEnglish
Article number012044
JournalJournal of Physics: Conference Series
Volume622
Issue number1
DOIs
Publication statusPublished - Jun 22 2015

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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