Optimization of reactor network design problem using Jumping Gene Adaptation of Differential Evolution

3rd International Conference on Science & Engineering in Mathematics, Chemistry and Physics 2015

数学;化学;物理学

Gujarathi, Ashish M.^1 ; Purohit, S.^2 ; Srikanth, B.^2

Petroleum and Chemical Engineering Department, Sultan Qaboos University, Muscat

PC 123, Oman^1

Birla Institute of Technology and Science, Pilani, India^2

关键词: Computational comparisons;    Differential Evolution;    Industrial processs;    Inequality constraint;    Optimal operating conditions;    Optimization of reactors;    Real-world problem;    Standard deviation;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/622/1/012044/pdf
DOI  :  10.1088/1742-6596/622/1/012044

来源: IOP

【 摘 要 】

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.