Training feedforward neural networks using hybrid flower pollination-gravitational search algorithm

Chakraborty, Dwaipayan; Saha, Sankhadip; Maity, Samaresh

doi:10.1109/ablaze.2015.7155008

Cited by 24 publications

(11 citation statements)

References 22 publications

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“…QIFPNN, FPNN, BANN, and PSONN were vulnerable to the parameters used, similar to other metaheuristic algorithms, which were also susceptible to their parameters. In this experiment, the population parameter 𝑛 = 30 [20] and the original search space for all dimensions 𝐿𝑏𝑅𝑒𝑎𝑙 = −80 and 𝑈𝑏𝑅𝑒𝑎𝑙 = 80 were determined. For FPNN, BANN and PSONN, the search space is directly performed against the original search space so that 𝐿𝑏 = 𝐿𝑏𝑅𝑒𝑎𝑙 = −80 and 𝑈𝑏 = 𝑈𝑏𝑅𝑒𝑎𝑙 = 80 .…”

Section: Parameter Settingsmentioning

confidence: 99%

Modified Flower Pollination Algorithm for Disease Identification in Swine

2021

IJIES

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Section: Parameter Settingsmentioning

confidence: 99%

Modified Flower Pollination Algorithm for Disease Identification in Swine

2021

IJIES

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“…This algorithm, as stimulated from nature, simulates the features of flowering plants and the important aspects that lead to find the global and the local feasible space. As for these features, it has been used to solve the optimal power flow problem [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Modified Flower Pollination Algorithm Constrained Optimal Power Flow

Hocine¹,

Salem²

2019

ISI

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In power systems, the power flow must satisfy a set of operational and safety requirements. It is a highly nonlinear problem to optimize the power flow. In this paper, the flower pollination algorithm (FPA) is modified and applied to optimize the power flow. Firstly, the author set up a mathematical model for power flow optimization problem, and formulated all the constraints. Next, the FPA was introduced in details, including the self-pollination, cross-pollination and other operations. After that, the local search strategy of the FPA was modified to replace the worst individuals in the current population with the best individuals in the previous population. Finally, the modified FPA (MFPA) was proved more efficient and better in convergence than standard algorithms like the firefly algorithm.

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“…The performance of the hybrid FPA was observed to be significantly superior to the performances of the FPA and DE algorithms individually. In solving numerical problems [44], the performances of feed-forward neural networks (FNNs) optimized by a FP-GSA outperform both the FPA and GSA in terms of classification accuracy. Thus, the FPA could be considered as a potential evolutionary algorithm to obtain better performance in diverse fields.…”

Section: Introductionmentioning

confidence: 99%

Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting

et al. 2016

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Abstract:Wind energy is increasingly considered one of the most promising sustainable energy sources for its characteristics of cleanliness without any pollution. Wind speed forecasting is a vital problem in wind power industry. However, individual forecasting models ignore the significance of data preprocessing and model parameter optimization, which may lead to poor forecasting performance. In this paper, a novel hybrid rk, B t s-ABBP (back propagation based on adaptive strategy with parameters k and B t ) model was developed based on an adaptive boosting (AB) strategy that integrates several BP (back propagation) neural networks for wind speed forecasting. The fast ensemble empirical mode decomposition technique is initially conducted in the preprocessing stage to reconstruct data, while a novel modified FPA (flower pollination algorithm) incorporating a conjugate gradient (CG) is proposed for searching for the optimal parameters of the rk, B t s-ABBP mode. The case studies of five wind power stations in Penglai, China are used as illustrative examples for evaluating the effectiveness and efficiency of the developed hybrid forecast strategy. Numerical results show that the developed hybrid model is simple and can satisfactorily approximate the actual wind speed series. Therefore, the developed hybrid model can be an effective tool in mining and analysis for wind power plants.

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Training feedforward neural networks using hybrid flower pollination-gravitational search algorithm

Cited by 24 publications

References 22 publications

Modified Flower Pollination Algorithm for Disease Identification in Swine

Modified Flower Pollination Algorithm for Disease Identification in Swine

Modified Flower Pollination Algorithm Constrained Optimal Power Flow

Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting

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