A Multi-Objective Black-Winged Kite Algorithm for Multi-UAV Cooperative Path Planning DOI Creative Commons

X.B. Liu,

Fufu Wang,

Yu Liu

et al.

Drones, Journal Year: 2025, Volume and Issue: 9(2), P. 118 - 118

Published: Feb. 5, 2025

In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach find a balanced solution that mitigates effects of subjective weighting, utilizing multi-objective optimization algorithm address complex planning issues involve multiple machines. Here, we introduce an advanced mathematical model cooperative path among UAVs in urban logistics scenarios, employing non-dominated sorting black-winged kite (NSBKA) this challenge. To evaluate efficacy NSBKA, was benchmarked against other algorithms using Zitzler, Deb, and Thiele (ZDT) test problems, Thiele, Laumanns, Zitzler (DTLZ) functions from conference on evolutionary computation 2009 (CEC2009) three types problems. Comparative analyses statistical results indicate proposed outperforms all 22 functions. verify capability NSBKA addressing multi-UAV problem model, applied solve problem. Simulation experiments five show can obtain reasonable collaborative set UAVs. Moreover, based generally superior terms energy saving, safety, computing efficiency during planning. This affirms effectiveness meta-heuristic dealing with objective cooperation problems further enhances robustness competitiveness NSBKA.

Language: Английский

Optimization of Variational Mode Decomposition-Convolutional Neural Network-Bidirectional Long Short Term Memory Rolling Bearing Fault Diagnosis Model Based on Improved Dung Beetle Optimizer Algorithm DOI Creative Commons
Weiqing Sun, Yue Wang, Xingyi You

et al.

Lubricants, Journal Year: 2024, Volume and Issue: 12(7), P. 239 - 239

Published: July 2, 2024

(1) Background: Rolling bearings are important components in mechanical equipment, but they also with a high failure rate. Once malfunction occurs, it will cause equipment to and may even affect personnel safety. Therefore, studying the fault diagnosis methods for rolling is of great significance current research hotspot frontier. However, vibration signals usually exhibit nonlinear non-stationary characteristics, easily affected by industrial environmental noise, making difficult accurately diagnose bearing faults. (2) Methods: this article proposes model based on an improved dung beetle optimizer (DBO) algorithm-optimized variational mode decomposition-convolutional neural network-bidirectional long short-term memory (VMD-CNN-BiLSTM). Firstly, DBO algorithm named CSADBO proposed integrating multiple strategies such as chaotic mapping cooperative search. Secondly, optimal parameter combination VMD was adaptively determined through algorithm, optimized used perform modal decomposition signal. Then, CNN-BiLSTM classification, hyperparameters were using algorithm. (3) Results: Finally, experiments conducted dataset Case Western Reserve University, method achieved average diagnostic accuracy 99.6%. (4) Conclusions: Experimental comparisons made other models verify effectiveness model. The experimental results show that VMD-CNN-BiLSTM can effectively be diagnosis, accuracy, provide theoretical reference related problems.

Language: Английский

Citations

5
Draco lizard optimizer: a novel metaheuristic algorithm for global optimization problems DOI
Xiaowei Wang

Evolutionary Intelligence, Journal Year: 2024, Volume and Issue: 18(1)

Published: Nov. 20, 2024

Language: Английский

Citations

5
Flexible interactive control method for multi-scenario sharing of hybrid pumped storage-wind-photovoltaic power generation DOI

Xiaojuan Han,

Fuxing Lv,

Jiarong Li

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 100, P. 113590 - 113590

Published: Sept. 5, 2024

Language: Английский

Citations

4
Somersault Foraging and Elite Opposition-Based Learning Dung Beetle Optimization Algorithm DOI Creative Commons
Daming Zhang, Zijian Wang, Fangjin Sun

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(19), P. 8624 - 8624

Published: Sept. 25, 2024

To tackle the shortcomings of Dung Beetle Optimization (DBO) Algorithm, which include slow convergence speed, an imbalance between exploration and exploitation, susceptibility to local optima, a Somersault Foraging Elite Opposition-Based Learning (SFEDBO) Algorithm is proposed. This algorithm utilizes elite opposition-based learning strategy as method for generating initial population, resulting in more diverse population. address exploitation algorithm, adaptive employed dynamically adjust number dung beetles eggs with each iteration Inspired by Manta Ray (MRFO) we utilize its somersault foraging perturb position optimal individual, thereby enhancing algorithm’s ability escape from optima. verify effectiveness proposed improvements, SFEDBO utilized optimize 23 benchmark test functions. The results show that achieves better solution accuracy stability, outperforming DBO terms optimization on Finally, was applied practical application problems pressure vessel design, tension/extension spring 3D unmanned aerial vehicle (UAV) path planning, were obtained. research shows this paper applicable actual has performance.

Language: Английский

Citations

4
Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm DOI Creative Commons

Juncheng Fu,

Zhengxiang Song, Jinhao Meng

et al.

Batteries, Journal Year: 2024, Volume and Issue: 10(11), P. 398 - 398

Published: Nov. 8, 2024

The accurate prediction of lithium-ion battery state health (SOH) can extend life, enhance device safety, and ensure sustained reliability in critical applications. Addressing the non-linear non-stationary characteristics capacity sequences, a novel method for predicting lithium SOH is proposed using deep hybrid kernel extreme learning machine (DHKELM) optimized by improved black-winged kite algorithm (IBKA). First, to address limitations traditional machines (ELMs) capturing features their poor generalization ability, concepts auto encoders (AEs) functions are introduced ELM, resulting establishment DHKELM model prediction. Next, tackle challenge parameter selection DHKELM, an optimal point set strategy, Gompertz growth model, Levy flight strategy employed optimize parameters IBKA before training. Finally, performance IBKA-DHKELM validated two distinct datasets from NASA CALCE, comparing it against BKA-DHKELM. results show that achieves smallest error, with RMSE only 0.0062, demonstrating exceptional fitting capability, high predictive accuracy, good robustness.

Language: Английский

Citations

4
Economic and Technical Aspects of Power Grids with Electric Vehicle Charge Stations, Sustainable Energies, and Compensators DOI Open Access
Minh Phuc Duong,

My-Ha Le,

Thang Trung Nguyen

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(1), P. 376 - 376

Published: Jan. 6, 2025

The study applies the black kite algorithm (BKA), equilibrium optimizer (EO), and secretary bird optimization (SBOA) to optimize placement of electric vehicle charge stations (EVCSs), wind turbine (WTSs), photovoltaic units (PVUs), capacitor banks (CAPBs) in IEEE 69-node distribution power grid. Three single objectives, including loss minimization, grid total voltage deviation improvement, are considered. For each objective function, five scenarios simulated under one operation hour, (1) place-only EVCSs; (2) place EVCSs PVUs; (3) EVCSs, PVUs, CAPBs; (4) WTSs; (5) WTSs, CAPBs. results indicate that EO can find best solutions for scenarios. SBOA two powerful algorithms optimal simulation cases. operating day, energy is supplied base loads 80,153.1 kWh, many nodes at high load factors violate lower limit 0.95 pu. As installing more renewable sources, need supply from 39,713.4 kWh. installed, demand continues be reduced 39,578.9 reduction greater than 50% all stations. Furthermore, significantly improved up higher pu, a few hours fall into lowest range. Thus, concludes economic technical aspects guaranteed DPGs with additional installation EVCSs.

Language: Английский

Citations

0
An innovative complex-valued encoding black-winged kite algorithm for global optimization DOI Creative Commons

Chengtao Du,

Jinzhong Zhang, Jie Fang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 6, 2025

Language: Английский

Citations

0
Enhanced Hyperspectral Forest Soil Organic Matter Prediction Using a Black-Winged Kite Algorithm-Optimized Convolutional Neural Network and Support Vector Machine DOI Creative Commons
Yun Deng, Linsong Xiao, Yuanyuan Shi

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 503 - 503

Published: Jan. 7, 2025

Soil Organic Matter (SOM) is crucial for soil fertility, and effective detection methods are of great significance the development agriculture forestry. This study uses 206 hyperspectral samples from state-owned Yachang Huangmian Forest Farms in Guangxi, using SPXY algorithm to partition dataset a 4:1 ratio, provide an spectral data preprocessing method novel SOM content prediction model area similar regions. Three denoising (no denoising, Savitzky–Golay filter discrete wavelet transform denoising) were combined with nine mathematical transformations (original reflectance (R), first-order differential (1DR), second-order (2DR), MSC, SNV, logR, (logR)′, 1/R, ((1/R)′) form 27 combinations. Through Pearson heatmap analysis modeling accuracy comparison, SG-1DR combination was found effectively highlight features. A CNN-SVM based on Black Kite Algorithm (BKA) proposed. leverages powerful parameter tuning capabilities BKA, CNN feature extraction, SVM classification regression, further improving prediction. The results RMSE = 3.042, R2 0.93, MAE 4.601, MARE 0.1, MBE 0.89, PRIQ 1.436.

Language: Английский

Citations

0
A novel improved Frilled Lizard algorithm for solving the optimal planning problem of renewable energy sources within distribution grids under uncertainties DOI
Badreddine Bendriss, Samir Sayah, A. Hamouda

et al.

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 326, P. 119465 - 119465

Published: Jan. 9, 2025

Language: Английский

Citations

0
Fuel Replenishment Problem of Heterogeneous Fleet in Initiative Distribution Mode DOI Open Access
Jin Li,

Hongying Song,

Huasheng Liu

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(2), P. 685 - 685

Published: Jan. 16, 2025

Petrol, a vital energy source for residents’ consumption and economically sustainable operation, generates substantial distribution demand. To reduce costs, we propose fuel replenishment problem using heterogeneous fleet based on the initiative mode. In this mode, center determines both delivery orders of customers plan. We develop mathematical model with minimal operational including transport, employment, penalty costs. A Two-stage heuristic algorithm K-IBKA time-space clustering is proposed, which also combines advantages butterfly optimization in quick convergence hierarchical mutation strategy population diversity. The results demonstrate that: (1) Heterogeneous truck exhibits better cost compared to homogeneous distribution, reducing total costs by 13.07%; (2) Compared passive mode reduced 11.03% 41.80%, respectively, through small large-scale instances. (3) unimproved BKA, ALNS, GA, calculated 37.68%, 35.30%, 27.26%, thus demonstrating contribution work petrol achieving development distribution.

Language: Английский

Citations

0