Optimisation of Biochemical Systems Production using Hybrid of Newton method, Differential Evolution Algorithm and Cooperative Coevolution Algorithm

نویسندگان

  • Mohd Arfian Ismail
  • Vitaliy Mezhuyev
  • Kohbalan Moorthy
  • Shahreen Kasim
  • Ashraf Osman Ibrahim
چکیده

This paper present a hybrid method of Newton method, Differential Evolution Algorithm (DE) and Cooperative Coevolution Algorithm (CCA). The proposed method is used to solve the optimisation problem in optimise the production of biochemical systems. The problems are maximising the biochemical systems production and simultaneously minimising the total amount of chemical reaction concentration involves. Besides that, the size of biochemical systems also contributed to the problem in optimising the biochemical systems production. In the proposed method, the Newton method is used in dealing biochemical system, DE for optimisation process while CCA is used to increase the performance of DE. In order to evaluate the performance of the proposed method, the proposed method is tested on two benchmark biochemical systems. Then, the result that obtained by the proposed method is compare with other works and the finding shows that the proposed method performs well compare to the other works.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

DAMAGE IDENTIFICATION IN STRUCTURES USING TIME DOMAIN RESPONSES BASED ON DIFFERENTIAL EVOLUTION ALGORITHM

An effective method utilizing the differential evolution algorithm (DEA) as an optimisation solver is suggested here to detect the location and extent of single and multiple damages in structural systems using time domain response method. Changes in acceleration response of structure are considered as a criterion for damage occurrence. The acceleration of structures is obtained using Newmark me...

متن کامل

Control of nonlinear systems using a hybrid APSO-BFO algorithm: An optimum design of PID controller

This paper proposes a novel hybrid algorithm namely APSO-BFO which combines merits of Bacterial Foraging Optimization (BFO) algorithm and Adaptive Particle Swarm Optimization (APSO) algorithm to determine the optimal PID parameters for control of nonlinear systems. To balance between exploration and exploitation, the proposed hybrid algorithm accomplishes global search over the whole search spa...

متن کامل

Control of nonlinear systems using a hybrid APSO-BFO algorithm: An optimum design of PID controller

This paper proposes a novel hybrid algorithm namely APSO-BFO which combines merits of Bacterial Foraging Optimization (BFO) algorithm and Adaptive Particle Swarm Optimization (APSO) algorithm to determine the optimal PID parameters for control of nonlinear systems. To balance between exploration and exploitation, the proposed hybrid algorithm accomplishes global search over the whole search spa...

متن کامل

An enhanced reliability-oriented workforce planning model for process industry using combined fuzzy goal programming and differential evolution approach

This paper draws on the “human reliability” concept as a structure for gaining insight into the maintenance workforce assessment in a process industry. Human reliability hinges on developing the reliability of humans to a threshold that guides the maintenance workforce to execute accurate decisions within the limits of resources and time allocations. This concept offers a worthwhile point of de...

متن کامل

Location and Capacity of UPFC to Improve the Operation Power System Using a Grasshopper Optimisation Algorithm

Unified Power Flow Controller (UPFC) is one of the most efficient FACTS devices that can be individually or combinatorial on all effective parameters in the transmission power of the transmission lines. With the flexibility of effective parameters in power passage, the power system operation can be improved. in this study, the optimization algorithm based on location and optimal capacity of UPF...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017