Short-Term Prediction of Rural Photovoltaic Power Generation Based on Improved Dung Beetle Optimization Algorithm

Sustainability, Journal Year: 2024, Volume and Issue: 16(13), P. 5467 - 5467

Published: June 27, 2024

Addressing the challenges of randomness, volatility, and low prediction accuracy in rural low-carbon photovoltaic (PV) power generation, along with its unique characteristics, is crucial for sustainable development energy. This paper presents a forecasting model that combines variational mode decomposition (VMD) an improved dung beetle optimization algorithm (IDBO) kernel extreme learning machine (KELM). Initially, Gaussian mixture (GMM) used to categorize PV data, separating analogous samples during different weather conditions. Afterwards, VMD applied stabilize initial sequence extract numerous consistent subsequences. These subsequences are then employed develop individual KELM models, their nuclear regularization parameters optimized by IDBO. Finally, predictions from various aggregated produce overall forecast. Empirical evidence via case study indicates proposed VMD-IDBO-KELM achieves commendable across diverse conditions, surpassing existing models affirming efficacy superiority. Compared traditional VMD-DBO-KELM algorithms, mean absolute percentage error on sunny days, cloudy days rainy reduced 2.66%, 1.98% 6.46%, respectively.

Language: Английский

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Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2024, Volume and Issue: 14(6)

Published: Aug. 18, 2024

Abstract This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects QLMA, including parameter adaptation, operator selection, and balancing global exploration local exploitation. QLMA has become a leading solution industries like energy, power systems, engineering, addressing range mathematical challenges. Looking forward, we suggest further integration, transfer learning strategies, techniques to reduce state space. article is categorized under: Technologies > Computational Intelligence Artificial

Language: Английский

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An Adaptive Spiral Strategy Dung Beetle Optimization Algorithm: Research and Applications

Biomimetics, Journal Year: 2024, Volume and Issue: 9(9), P. 519 - 519

Published: Aug. 29, 2024

The Dung Beetle Optimization (DBO) algorithm, a well-established swarm intelligence technique, has shown considerable promise in solving complex engineering design challenges. However, it is hampered by limitations such as suboptimal population initialization, sluggish search speeds, and restricted global exploration capabilities. To overcome these shortcomings, we propose an enhanced version termed Adaptive Spiral Strategy (ADBO). Key enhancements include the application of Gaussian Chaos strategy for more effective integration Whale Search inspired Algorithm, introduction adaptive weight factor to improve efficiency enhance These improvements collectively elevate performance DBO significantly enhancing its ability address intricate real-world problems. We evaluate ADBO algorithm against suite benchmark algorithms using CEC2017 test functions, demonstrating superiority. Furthermore, validate effectiveness through applications diverse domains robot manipulator design, triangular linkage problems, unmanned aerial vehicle (UAV) path planning, highlighting impact on improving UAV safety energy efficiency.

Language: Английский

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Somersault Foraging and Elite Opposition-Based Learning Dung Beetle Optimization Algorithm

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

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Construction of Orchard Agricultural Machinery Dispatching Model Based on Improved Beetle Optimization Algorithm

Agronomy, Journal Year: 2025, Volume and Issue: 15(2), P. 323 - 323

Published: Jan. 27, 2025

In order to enhance orchard agricultural efficiency and lower fruit production expenses, we propose a BL-DBO (Beetle Optimization Algorithm introducing Bernoulli mapping Lévy flights) solve the machinery dispatching model within area. First, analyze problem in area establish its mathematical with objective of minimizing costs as constraint. To tackle problems uneven individual position distribution risk becoming stuck local optimal solutions traditional DBO algorithm, introduce during initialization phase DBO. This method ensures uniform initialized population. Furthermore, iterative process incorporated flight approach into positional update equations for beetles involved breeding, foraging, theft activities helps escape from solutions. Finally, conduct experiments based on location information Shunping Shunnong Orchard trees Shijiazhuang. The results indicate that, compared using human experience generated by not only reduce number purchases but also decrease energy loss non-working distances machinery, effectively saving costs.

Language: Английский

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A novel modelling method for rolling force prediction based on deep stochastic configuration networks fused with physical knowledge

Information Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 122097 - 122097

Published: March 1, 2025

Language: Английский

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Path planning for greenhouse robots using a hybrid Dung beetle algorithm

Intelligent Service Robotics, Journal Year: 2025, Volume and Issue: unknown

Published: April 12, 2025

Language: Английский

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Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

Electronics, Journal Year: 2023, Volume and Issue: 12(21), P. 4462 - 4462

Published: Oct. 30, 2023

The Dung Beetle Optimization (DBO) algorithm is a powerful metaheuristic that widely used for optimization problems. However, the DBO has limitations in balancing global exploration and local exploitation capabilities, often leading to getting stuck optima. To overcome these address problems, this study introduces Multi-Strategy Improved (MSIDBO) Algorithm. MSIDBO incorporates several advanced computational techniques enhance its performance. Firstly, it random reverse learning strategy improve population diversity mitigate early convergence or stagnation issues present algorithm. Additionally, fitness-distance employed better manage trade-off between within population. Furthermore, utilizes spiral foraging precision, promote strong exploratory prevent being trapped further search ability particle utilization of algorithm, combines Optimal Dimension-Wise Gaussian Mutation strategy. By minimizing premature convergence, increased, accelerated. This expansion space reduces likelihood optima during evolutionary process. demonstrate effectiveness extensive experiments are conducted using benchmark test functions, comparing performance against other well-known algorithms. results highlight feasibility superiority solving Moreover, applied path planning simulation showcase practical application potential. A comparison with shows generates shorter faster paths, effectively addressing real-world

Language: Английский

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An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets

Biomimetics, Journal Year: 2024, Volume and Issue: 9(9), P. 533 - 533

Published: Sept. 4, 2024

The Internet's development has prompted social media to become an essential channel for disseminating disaster-related information. Increasing the accuracy of emotional polarity recognition in tweets is conducive government or rescue organizations understanding public's demands and responding appropriately. Existing sentiment analysis models have some limitations applicability. Therefore, this research proposes IDBO-CNN-BiLSTM model combining swarm intelligence optimization algorithm deep learning methods. First, Dung Beetle Optimization (DBO) improved by adopting Latin hypercube sampling, integrating Osprey Algorithm (OOA), introducing adaptive Gaussian-Cauchy mixture mutation disturbance. DBO (IDBO) then utilized optimize Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model's hyperparameters. Finally, constructed classify tendencies associated with Hurricane Harvey event. empirical indicates that proposed achieves 0.8033, outperforming other single hybrid models. In contrast GWO, WOA, algorithms, enhanced 2.89%, 2.82%, 2.72%, respectively. This study proves can be applied assist emergency decision-making natural disasters.

Language: Английский

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Optimization of vehicle conceptual design problems using an enhanced hunger games search algorithm

Materials Testing, Journal Year: 2024, Volume and Issue: 66(11), P. 1864 - 1889

Published: Oct. 15, 2024

Abstract Electric vehicles have become a standard means of transportation in the last 10 years. This paper aims to formalize design optimization problems for electric vehicle components. It presents tool conceptual technique with hunger games search optimizer that incorporates dynamic adversary-based learning and diversity leader (referred as HGS-DOL-DIL) overcome local optimum trap low convergence rate limitations Hunger Games algorithm improve rate. The performance proposed algorithms is studied on six widely used engineering problems, complex constraints, discrete variables. For HGS-DOL-DIL practical feasibility analysis, case study shape an car suspension arm from industry carried out. Overall, inclusion OL strategy has proven its superiority solving real-world especially such automobile arm, showing improves space solution quality, reflects potential find global solutions well-balanced exploration exploitation phase.

Language: Английский

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