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 language | English |
|---|---|
| Article number | 012044 |
| Journal | Journal of Physics: Conference Series |
| Volume | 622 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jun 22 2015 |
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ASJC Scopus subject areas
- Physics and Astronomy(all)
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Optimization of reactor network design problem using jumping gene adaptation of differential evolution. / Gujarathi, Ashish M.; Purohit, S.; Srikanth, B.
In: Journal of Physics: Conference Series, Vol. 622, No. 1, 012044, 22.06.2015.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Optimization of reactor network design problem using jumping gene adaptation of differential evolution
AU - Gujarathi, Ashish M.
AU - Purohit, S.
AU - Srikanth, B.
PY - 2015/6/22
Y1 - 2015/6/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84938828888&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84938828888&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/622/1/012044
DO - 10.1088/1742-6596/622/1/012044
M3 - Article
AN - SCOPUS:84938828888
VL - 622
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 1
M1 - 012044
ER -