A modified whale optimization algorithm to overcome delayed convergence in artificial neural networks

Kushwah, Rashmi; Kaushik, Manika; Chugh, Kashish

doi:10.1007/s00500-021-05983-z

Cited by 19 publications

(9 citation statements)

References 57 publications

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“…As improving the conventional ANN classifier using the optimized bio-inspired algorithm WOA is the key contribution of this paper, researchers have reviewed some recent research articles [40][41][42][43][44][45][46][47] on improved WOA in this section. [40] presented the idea of a leader to direct the populace toward the ideal solution area and quicken WOA convergence.…”

Section: B Improving Woa Methodsmentioning

confidence: 99%

“…Third, a special nonlinearly controlled parameter factor built on the logarithm function had been provided to strike a balance between exploration and exploitation. In [46], four primary operators were proposed to [47] to address the issue of delayed convergence and to strike a balance between exploration and exploitation. In M-WOA, the choice of a roulette wheel had been combined with WOA to speed up convergence.…”

Section: B Improving Woa Methodsmentioning

confidence: 99%

“…Also, the conventional optimization algorithms failed to overcome the challenges of slow convergence and quickly into local optima for the MDDS dataset. • The improved bio-inspired algorithm WOA had been proposed in research articles [40][41][42][43][44][45][46][47], but none of these methods have been applied to the energy consumption forecasting domain. Except for the research article [47], none of the improved WOA techniques were applied to machine learning algorithms.…”

Section: Research Gaps Analysismentioning

confidence: 99%

“…• The improved bio-inspired algorithm WOA had been proposed in research articles [40][41][42][43][44][45][46][47], but none of these methods have been applied to the energy consumption forecasting domain. Except for the research article [47], none of the improved WOA techniques were applied to machine learning algorithms. This research article proposes the CAC-OWOA predictive model to overcome the above research challenge.…”

Section: Research Gaps Analysismentioning

confidence: 99%

“…Researchers have analyzed the performance of OWOA with basic WOA, SWWOA [45], IWOA [46], and M-WOA [47] models. All these models are evaluated regarding convergence time, training accuracy, and MSE rate for the 70 % training sample.…”

Section: State-of-the-art Analysismentioning

confidence: 99%

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Context-Aware Clustering and the Optimized Whale Optimization Algorithm: An Effective Predictive Model for the Smart Grid

Ahire

Patil

2023

IJRITCC

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For customers to participate in key peak pricing, period-of-use fees, and individualized responsiveness to demand programmes taken from multi-dimensional data flows, energy use projection and analysis must be done well. However, it is a difficult study topic to ascertain the knowledge of use of electricity as recorded in the electricity records' Multi-Dimensional Data Streams (MDDS). Context-Aware Clustering (CAC) and the Optimized Whale Optimization Algorithm were suggested by researchers as a fresh power usage knowledge finding model from the multi-dimensional data streams (MDDS) to resolve issue (OWOA). The proposed CAC-OWOA framework first performs the data cleaning to handle the noisy and null elements. The predictive features are extracted from the novel context-aware group formation algorithm using the statistical context parameters from the pre-processed MDDS electricity logs. To perform the energy consumption prediction, researchers have proposed the novel Artificial Neural Network (ANN) predictive algorithm using the bio-inspired optimization algorithm called OWOA. The OWOA is the modified algorithm of the existing WOA to overcome the problems of slow convergence speed and easily falling into the local optimal solutions. The ANN training method is used in conjunction with the suggested bio-inspired OWOA algorithm to lower error rates and boost overall prediction accuracy. The efficiency of the CAC-OWOA framework is evaluated using the publicly available smart grid electricity consumption logs. The experimental results demonstrate the effectiveness of the CAC-OWOA framework in terms of forecasting accuracy, precision, recall, and duration when compared to underlying approaches.

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