“…[67]. Since the search relies entirely on a random search on (12), a faster convergence is not guaranteed. The proposed method presented here brings two changes to tackle the multimodal optimization problem, making it more practical for a range of applications without losing the attractive features of the original technique.…”
Well placement optimization is considered a non-convex and highly multimodal optimization problem. In this article, a modified crow search algorithm is proposed to tackle the well placement optimization problem. This article proposes modifications based on local search and niching techniques in the crow search algorithm (CSA). At first, the suggested approach is verified by experimenting with the benchmark functions. For test functions, the results of the proposed approach demonstrated a higher convergence rate and a better solution. Again, the performance of the proposed technique is evaluated with well placement optimization problem and compared with particle swarm optimization (PSO), the Gravitational Search Algorithm (GSA), and the Crow search algorithm (CSA). The outcomes of the study revealed that the niching crow search algorithm is the most efficient and effective compared to the other techniques.
“…[67]. Since the search relies entirely on a random search on (12), a faster convergence is not guaranteed. The proposed method presented here brings two changes to tackle the multimodal optimization problem, making it more practical for a range of applications without losing the attractive features of the original technique.…”
Well placement optimization is considered a non-convex and highly multimodal optimization problem. In this article, a modified crow search algorithm is proposed to tackle the well placement optimization problem. This article proposes modifications based on local search and niching techniques in the crow search algorithm (CSA). At first, the suggested approach is verified by experimenting with the benchmark functions. For test functions, the results of the proposed approach demonstrated a higher convergence rate and a better solution. Again, the performance of the proposed technique is evaluated with well placement optimization problem and compared with particle swarm optimization (PSO), the Gravitational Search Algorithm (GSA), and the Crow search algorithm (CSA). The outcomes of the study revealed that the niching crow search algorithm is the most efficient and effective compared to the other techniques.
“…Kaya and Zarrouk [ 31 ] point out that this approach can work and increase reservoir pressure after 10 years of steam production; however, increasing exploitation may affect steam production. New approaches such as artificial neural networks have begun to be used for reinjection well placement and optimization since the beginning of the 2000s [ 18 , 34 , 35 ]. Fig.…”
Section: Importance Of Sustainable Development At Geothermal Power Symentioning
Effective geothermal power generation depends on two main elements: geothermal reservoir management and maintenance of the power plant. Reservoir management consists of both the fluid production and reinjection of brine to the underground. The management of wells is important to ensure the sustainability of the reservoir. Thus, the flow rate control systems are essential to protect geothermal reservoirs under long-term power production. The second issue is the daily change in electricity prices and the load change process is complex because geothermal well controls are not flexible operations. The well management thus requires control approaches, and fuzzy control can be one effective solution. In this study, a fuzzy control system has been developed to control flow rates of the wells in Kızıldere geothermal field and its performance has been compared with the real data taken from the Kızıldere Power Plant. The results of comparison show that the fuzzy controllers achieved the target energy production in 2 h instead of 5 h, compared to the real data. Based on the real data, the reinjection was only able to stabilize at the end of the fourth hour and the process took only 2 h when using the fuzzy controllers.
“…Assuming aforementioned notations for a uniform value, a t-way test suite generation problem can be written as, CA (N; t, v k ) where N is the test cases with t interaction strength for k number of parameters with v values. When t = 2, k = 4, and v = 3, the aforementioned expression can be rewritten as: CA(N; 2, 3 4 In our experiments, 16 different configurations combining both CA and MCA are taken into account similar to that of the earlier work in [58]: CA (N; 2, 3 4 ), CA(N; 2, 3 13 ), CA(N; 2, 10 10 ), CA(N; 2, 15 10 ), CA(N; 2, 5 10 ), CA(N, 2, 8 10 ), CA(N; 3, 3 6 ), CA(N; 3, 4 6 ), CA(N; 3, 5 6 ), CA(N; 3, 6 6 ), CA(N; 3, 5 7 ), CA (N; 3, 10 6 ), MCA(N; 2, 5 1 3 8 2 2 ), MCA(N; 2, 7 1…”
Section: B Combinatorial Test Suite Generationmentioning
confidence: 99%
“…An optimization problem refers to the problem of finding the best solution from a set of candidate solutions. Generally, an optimization algorithm exploits a mathematical function, called the objective function, which is often an extremum (either maximum or minimum) function for finding an optimal solution while satisfying a set of given constraints [1][2][3][4].…”
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