Cuckoo search algorithm for solving numerical integration

Deng, Zexi; Huang, Feidan

doi:10.1109/cyber.2015.7288168

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…Therefore, the total FCS system affected the total power loss, and LVD can be moderated by installing the optimum PV. The low penetration level from the minimization of the FCSs is revealed by the closing values of 50 kW of each position, similar to that presented in [23], but the LVD and the PV position are different from the randomized FCSs and PV position. Figure 8 shows the comparison of the total FCS sizing against the total power loss and LVD of the SAA.…”

Section: Simulation and Resultssupporting

confidence: 66%

“…The cuckoo makes its egg (λ) as similar as possible to the nest, trying to avoid the nest owner finding any foreign eggs, sometimes called an alien egg. If the owner finds the egg, there are two options: destroy foreign eggs by throwing away the cuckoo eggs, or the nest birds leave the nest (Pa) to build a new nest [23]. In nature, creatures can seek nourishment in an arbitrary or quasi-random way.…”

Section: Cuckoo Search Algorithm (Csa)mentioning

confidence: 99%

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Optimal Placement of Distributed Photovoltaic Systems and Electric Vehicle Charging Stations Using Metaheuristic Optimization Techniques

et al. 2021

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In this study, the concept of symmetry is introduced by finding the optimal state of a power system. An electric vehicle type load is present, where the supply stores’ electrical energy causes an imbalance in the system. The optimal conditions are related by adjusting the voltage of the bus location. The key variables are the load voltage deviation (LVD), the variation of the load and the power, and the sizing of the distributed photovoltaic (DPV), which are added to the system for power stability. Here, a method to optimize the fast-charging stations (FCSs) and DPV is presented using an optimization technique comparison. The system tests the distribution line according to the bus grouping in the IEEE 33 bus system. This research presents a hypothesis to solve the problem of the voltage level in the system using metaheuristic algorithms: the cuckoo search algorithm (CSA), genetic algorithm (GA), and simulated annealing algorithm (SAA) are used to determine the optimal position for DPV deployment in the grid with the FCSs. The LVD, computation time, and total power loss for each iteration are compared. The voltage dependence power flow is applied using the backward/forward sweep method (BFS). The LVD is applied to define the objective function of the optimization techniques. The simulation results show that the SAA showed the lowest mean computation time, followed by the GA and the CSA. A possible location of the DPV is bus no. 6 for FCSs with high penetration levels, and the best FCS locations can be found with the GA, with the best percentage of best hit counter on buses no. 2, 3, 13, 14, 28, 15, and 27. Therefore, FCSs can be managed and handled in optimal conditions, and this work supports future FCS expansion.

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Section: Simulation and Resultssupporting

confidence: 66%

Section: Cuckoo Search Algorithm (Csa)mentioning

confidence: 99%

Optimal Placement of Distributed Photovoltaic Systems and Electric Vehicle Charging Stations Using Metaheuristic Optimization Techniques

et al. 2021

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“…Work on improving the algorithm is under progress [12]. Despite the above, the CS was tested in practical applications [13][14][15][16][17][18][19].…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Comparison of using the genetic algorithm and cuckoo search for multicriteria optimisation with limitation

Klempka¹,

Filipowicz²

2017

Turk J Elec Eng & Comp Sci

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Abstract:The article presents an example of using two optimisation methods, a genetic algorithm and cuckoo search, to identify parameters of electric drive controllers using some quality criteria and by applying a limitation to the maximum values of signals in the controlled facility. The results for both optimisation methods are compared. The impact of the probability that the nest host discovers the laid eggs on the speed of finding the optimum solution is investigated.

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“…Numerical simulation has been used in wide range of applications [6,8]. FPGA in an attractive platform for the computation from electronics applications to engine system [7].…”

Section: Related Workmentioning

confidence: 99%

High speed numerical integration algorithm using FPGA

Razak¹,

Talip²,

Yakub³

et al. 2018

J. Fundam and Appl Sci.

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Conventionally, numerical integration to accomplish. Field Programmable Gate Arrays (FPGAs) can be used as a efficient and reliable alternative to implement the proposed a hardware implementation of four numerical integration algorithms using FPGA. The computation is based on Left Riemann Sum (LRS), Right Riemann Sum (RRS), MiddleRiemann Sum (MRS) and Trapezoidal Sum (TS) algorithms. The system performance is evaluated based on target chip Altera Cyclone IV FPGA in the metrics of resources utilization, clock latency, execution time, power consumption and computational error compared to the other algorithms. The result also shows execution time of the FPGA are much faster compared to the software implementation.

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Cuckoo search algorithm for solving numerical integration

Cited by 7 publications

References 8 publications

Optimal Placement of Distributed Photovoltaic Systems and Electric Vehicle Charging Stations Using Metaheuristic Optimization Techniques

Optimal Placement of Distributed Photovoltaic Systems and Electric Vehicle Charging Stations Using Metaheuristic Optimization Techniques

Comparison of using the genetic algorithm and cuckoo search for multicriteria optimisation with limitation

High speed numerical integration algorithm using FPGA

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