The Application of Genetic Algorithm with Multi-parent Crossover to Optimal Power Flow Problem

Srihari, T.; Boppa, Madhu; Kumar, S. Anil; Pulluri, Harish

doi:10.1007/978-981-15-2256-7_40

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Genetic algorithm initially presented by John Holland in 1960 [14]. So far GA has been applied to solve various real world issues [15][16][17][18][19][20][21][22]. It is capable of reaching the global minimum search area in a quick period and takes longer to converge.…”

Section: Preamblementioning

confidence: 99%

Solution of OPF Using GA Variant With Inclusion of FACTS Devices

Priyanka,

Pulluri,

Venkateshwarlu

2024

E3S Web Conf.

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Optimal power flow (OPF) is a tool used for minimization through that secured and cost-effective power systems is obtained with the inclusion of FACTS devices for existing power systems that enhance the power transfer ability of the power system, which reduces congestion. The current work proposed a solution to the OPF issue in power systems using genetic algorithm (GA). The proposed technique is implemented on an IEEE 30 bus system by considering the minimization of fuel cost and L-index functions. The results at the end of this paper pre-sent the efficacy of GA algorithm to solve OPF issues in power systems and FACTS devices while comparing it to other algorithms presented previous re-search.

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Section: Preamblementioning

confidence: 99%

Solution of OPF Using GA Variant With Inclusion of FACTS Devices

Priyanka,

Pulluri,

Venkateshwarlu

2024

E3S Web Conf.

Self Cite

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“…GA-MPC is a modified GA that proposes a new crossover method [49]. It has been used to effectively solve an economic dispatch problem [50] and an optimal power flow problem [51]. It has also been applied to solve an inverse problem in RDDEs [52].…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation in Neutral Delay Differential Equations Using Genetic Algorithm With Multi-Parent Crossover

Jamilla

Mendoza

2021

IEEE Access

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Neutral delay differential equations (NDDEs) are differential equations containing time lags not only in the states but also in the state derivatives. NDDEs have applications in modeling physical and biological systems. An NDDE model may consist of several parameters, some of which can be determined using available data. Various numerical techniques have been studied to estimate parameters of mathematical models. In this study, parameter estimation is posed as an optimization problem. The use of heuristic algorithms in parameter estimation has gained popularity because of its ease of implementation, requiring only function evaluations. But to our knowledge, heuristic algorithms have never been employed in estimating parameters in NDDE models. In this work, we apply Genetic Algorithm with Multi-Parent Crossover (GA-MPC) to obtain parameter estimates of three NDDE models with a discrete delay. We compare the estimates to those obtained using standard heuristic algorithms. Results show that GA-MPC is capable of consistently identifying model parameters that provide a good fit of the model to the data.INDEX TERMS neutral delay differential equations, parameter estimation, genetic algorithm, bootstrapping, inverse problem, heuristic algorithms VOLUME x, 20xx

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Philippine Eagle Optimization Algorithm

2022

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We propose the Philippine Eagle Optimization Algorithm (PEOA), which is a meta-heuristic and population-based search algorithm inspired by the territorial hunting behavior of the Philippine Eagle. From an initial random population of eagles in a given search space, the best eagle is selected and undergoes a local food search using the interior point method as its means of exploitation. The population is then divided into three subpopulations, and each subpopulation is assigned an operator which aids in the exploration. Once the respective operators are applied, the new eagles with improved function values replace the older ones. The best eagle of the population is then updated and conducts a local food search again. These steps are done iteratively, and the food searched by the final best eagle is the optimal solution of the search space. PEOA is tested on 20 optimization test functions with different modality, separability, and dimension properties. The performance of PEOA is compared to 13 other optimization algorithms. To further validate the effectiveness of PEOA, it is also applied to image reconstruction in electrical impedance tomography and parameter identification in a neutral delay differential equation model. Numerical results show that PEOA can obtain accurate solutions to various functions and problems. PEOA proves to be the most computationally inexpensive algorithm relative to the others examined, while also helping promote the critically endangered Philippine Eagle.

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The Application of Genetic Algorithm with Multi-parent Crossover to Optimal Power Flow Problem

Cited by 3 publications

References 8 publications

Solution of OPF Using GA Variant With Inclusion of FACTS Devices

Solution of OPF Using GA Variant With Inclusion of FACTS Devices

Parameter Estimation in Neutral Delay Differential Equations Using Genetic Algorithm With Multi-Parent Crossover

Philippine Eagle Optimization Algorithm

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