Dynamic Hunting Leadership optimization: Algorithm and applications

Journal of Computational Science, Год журнала: 2023, Номер 69, С. 102010 - 102010

Опубликована: Март 31, 2023

The Dynamic Hunting Leadership (DHL) algorithm is an innovative heuristic technique that draws inspiration from nature to find almost optimal solutions for various optimization problems. It consists of four variants, each highlighting distinct leadership strategies guide the hunting process. development was based on realization effective during process can significantly improve its efficacy. concept behind these methods dynamically modify number leaders, which enhance algorithm's performance. stability DHL variants in exploring unknown area search space and exploitation phases compared, advantages exploration or ability different are discussed. Moreover, results compared with more than twenty well-known algorithms. efficacy proposed algorithms discovering near-optimal tested across several real-world applications, outcomes demonstrate outperforms other competing distinctiveness identify global minimum benchmark problems superior performance enhancing objective value welded beam design problem tension–compression spring problem, surpassing values achieved by algorithms' also allocation distributed generation (DG) energy storage system (ESS) balanced electrical distribution systems. show all obtained problem. For control strategy voltage regulators three-phase unbalanced power systems, improved 35.7% best found Based analysis comparison convergence behavior benchmarks problems, method proves be reliable method.

Язык: Английский

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A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

International journal of intelligent engineering and systems, Год журнала: 2023, Номер 16(3), С. 345 - 361

Опубликована: Май 1, 2023

This research presents a novel hybrid sampling technique, implemented at the data level, to effectively address imbalanced and noisy in classification processes.The proposed technique expertly combines two established methods, namely, random over (ROS) neighbourhood cleaning rule (NCL) approaches, tackle imbalance noise issues, respectively.The study carried out an empirical evaluation of approach using crowdsourced text that primarily emphasized triple bottom line (TBL) dimension smart social, economic, environmental city.The used long short-term memory (LSTM), convolutional neural networks (CNN), CNN-LSTM models validate efficacy compare its performance with other existing including ROS oversampling, NCL undersampling, synthetic minority & tomek links (SMOTE-Tomek), oversampling edited nearest neighbours (SMOTE-ENN) sampling.The results are impressive, ROS-NCL achieving high accuracy rates across all three models, 97.71%, 98.01%, 98.11%, respectively.This provides robust effective solution for handling impure holds great promise identifying complex patterns real-world problems.

Язык: Английский

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Development of MCS based-ensemble models using CEEMDAN decomposition and machine intelligence

Intelligent Systems with Applications, Год журнала: 2023, Номер 18, С. 200202 - 200202

Опубликована: Фев. 15, 2023

In this paper, stock price data has been predicted using several state-of-the-art methodologies such as stochastic models, machine learning techniqus, and deep algorithms. An efficient decomposition method resonating with these Machine Intelligence (MI) models embedded boosting ensemble method. Finally a Model Confidence Set (MCS) based algorithm proposed for forecasting data. Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) decomposed orthogonal subseries have Random Forests (RFs). Then Kernel Ridge Regression (KRR) model is used to combine those predictions form hybrid predictor. addition, improvement in prediction performance observed kernel functions. Boosting (AdaBoost) found stimulating accuracy of Long Short-Term Memory (LSTM) Gated Recurrent Unit (GRU) models. CEEMDAN also increased the AdaBoost. Nevertheless, combination forecasts from various good approach improving result. Despite optimizing weights all heuristic MCS-based snuffing least important prior averaging conceded potent approach. MCS rescinds insignificant on out-of-sample or in-sample equally average superior The compared existing standalone techniques validation measures. However, Support Vector (CCEMDAN_SVR) be best predictor current scenario.

Язык: Английский

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Neuromorphic electronics for robotic perception, navigation and control: A survey

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 126, С. 106838 - 106838

Опубликована: Авг. 8, 2023

Язык: Английский

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Advancements in optimizing wave energy converter geometry utilizing metaheuristic algorithms

Renewable and Sustainable Energy Reviews, Год журнала: 2024, Номер 197, С. 114398 - 114398

Опубликована: Апрель 3, 2024

Язык: Английский

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