A synergy between the genetic algorithm and simulated annealing in a gas allocation optimization problem

V. Sawadogo*, A. Diallo

Department of Engineering, Burkina Institute of Technology, B.P. 322 Koudougou, Burkina Faso


Usually, in gas lift operation there is a limited amount of lift gas that should be allocated between some wells in a way that the total produced oil maximized. For this purpose, different optimization algorithms (such as a genetic algorithm) are used.  Generally, these algorithms have different internal parameters based on them; the resulted optimum point is affected. To find the best optimizer’s parameters, it is usual to change one parameter and set other ones to a constant value, and again change another parameter and set others to a fixed value. This method needs the different runs of the optimizer (with different optimizer parameters) and it is clear that it is very time-consuming. Here is a new approach simulated annealing has coupled with a genetic algorithm. The genetic algorithm optimizes the gas allocation rates and simultaneously simulated annealing optimizes the genetic algorithm parameters. Results show that this new mean is much faster than the changing variable method, as well as the quality of its optimum point, which is much better than other methods (changing variable method).


Gas lift, Changing variable method, Parameter control, Parameter tuning

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Article history

Received 10 May 2019, Received in revised form 11 October 2019, Accepted 18 October 2019

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Sawadogo V and Diallo A (2020). A synergy between the genetic algorithm and simulated annealing in a gas allocation optimization problem. Annals of Electrical and Electronic Engineering, 3(1): 1-7

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