An improved sparrow search algorithm based on quantum computations and multi-strategy enhancement

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 215, P. 119421 - 119421

Published: Dec. 9, 2022

Language: Английский

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African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems

Sustainability, Journal Year: 2022, Volume and Issue: 14(13), P. 8172 - 8172

Published: July 4, 2022

An effective maximum power point tracking (MPPT) technique plays a crucial role in improving the efficiency and performance of grid-connected renewable energy sources (RESs). This paper uses African Vulture Optimization Algorithm (AVOA), metaheuristic inspired by nature, to tune proportional–integral (PI)-based MPPT controllers for hybrid RESs solar photovoltaic (PV) wind systems, as well PI storage system that are used smooth output fluctuations those system. The AVOA is compared with widely particle swarm optimization (PSO) technique, which commonly acknowledged foundation intelligence. As result, this introduced study draw comparison. It observed proposed algorithm outperformed PSO terms speed, robustness, best convergence minimum value. A MATLAB/Simulink model was built, simulation were carried out verify algorithms. In conclusion, results showed promising method solving variety engineering problems.

Language: Английский

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IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems

Mathematical Biosciences & Engineering, Journal Year: 2022, Volume and Issue: 19(11), P. 10963 - 11017

Published: Jan. 1, 2022

<abstract><p>Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) are two newly developed meta-heuristic algorithms that simulate several intelligent hunting behaviors of Aquila vulture in nature, respectively. AO has powerful global exploration capability, whereas its local exploitation phase is not stable enough. On the other hand, AVOA possesses promising capability but insufficient mechanisms. Based on characteristics both algorithms, this paper, we propose an improved hybrid optimizer called IHAOAVOA to overcome deficiencies single algorithm provide higher-quality solutions for solving optimization problems. First, combined retain valuable search competence each. Then, a new composite opposition-based learning (COBL) designed increase population diversity help escape from optima. In addition, more effectively guide process balance exploitation, fitness-distance (FDB) selection strategy introduced modify core position update formula. The performance proposed comprehensively investigated analyzed by comparing against basic AO, AVOA, six state-of-the-art 23 classical benchmark functions IEEE CEC2019 test suite. Experimental results demonstrate achieves superior solution accuracy, convergence speed, optima avoidance than comparison methods most functions. Furthermore, practicality highlighted five engineering design Our findings reveal technique also highly competitive when addressing real-world tasks. source code publicly available at <a href="https://doi.org/10.24433/CO.2373662.v1" target="_blank">https://doi.org/10.24433/CO.2373662.v1</a>.</p></abstract>

Language: Английский

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A robust chaos-inspired artificial intelligence model for dealing with nonlinear dynamics in wind speed forecasting

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e2393 - e2393

Published: Oct. 10, 2024

The global impacts of climate change have become increasingly pronounced in recent years due to the rise greenhouse gas emissions from fossil fuels. This trend threatens water resources, ecological balance, and could lead desertification drought. To address these challenges, reducing fuel consumption embracing renewable energy sources is crucial. Among these, wind stands out as a clean source garnering more attention each day. However, variable unpredictable nature speed presents challenge integrating into electricity grid. Accurate forecasting essential overcome obstacles optimize usage. study focuses on developing robust model capable handling non-linear dynamics minimize losses improve efficiency. Wind data Bandırma meteorological station Marmara region Turkey, known for its potential, was decomposed intrinsic mode functions (IMFs) using empirical decomposition (REMD). extracted IMFs were then fed long short-term memory (LSTM) architecture whose parameters estimated African vultures optimization (AVO) algorithm based tent chaotic mapping. approach aimed build highly accurate model. performance proposed improving compared with that particle swarm (CPSO) algorithm. Finally, highlights potential utilizing advanced techniques deep learning models forecasting, ultimately contributing efficient sustainable generation. hybrid represents significant step forward research practical applications.

Language: Английский

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Recent applications and advances of African Vultures Optimization Algorithm

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(12)

Published: Oct. 17, 2024

Language: Английский

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Metaheuristics for Solving Global and Engineering Optimization Problems: Review, Applications, Open Issues and Challenges

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(8), P. 4485 - 4519

Published: Aug. 21, 2024

Abstract The greatest and fastest advances in the computing world today require researchers to develop new problem-solving techniques capable of providing an optimal global solution considering a set aspects restrictions. Due superiority metaheuristic Algorithms (MAs) solving different classes problems promising results, MAs need be studied. Numerous studies algorithms fields exist, but this study, comprehensive review MAs, its nature, types, applications, open issues are introduced detail. Specifically, we introduce metaheuristics' advantages over other techniques. To obtain entire view about classifications based on (i.e., inspiration source, number search agents, updating mechanisms followed by agents their positions, primary parameters algorithms) presented detail, along with optimization including both structure types. application area occupies lot research, so most widely used applications presented. Finally, great effort research is directed discuss challenges which help upcoming know future directions active field. Overall, study helps existing understand basic information field addition directing newcomers areas that addressed future.

Language: Английский

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Evaluation and Prediction of Agricultural Water Use Efficiency in the Jianghan Plain Based on the Tent-SSA-BPNN Model

Agriculture, Journal Year: 2025, Volume and Issue: 15(2), P. 140 - 140

Published: Jan. 9, 2025

The Jianghan Plain (JHP) is a key agricultural area in China where efficient water use (AWUE) vital for sustainable management, food security, environmental sustainability, and economic growth. This study introduces novel AWUE prediction model the JHP, combining BP neural network with Sparrow Search Algorithm (SSA) an improved Tent Mixing (Tent-SSA-BPNN). hybrid addresses limitations of traditional methods by enhancing forecast accuracy stability. By integrating historical data factors, provides detailed understanding AWUE’s spatial temporal variations. Compared to networks other methods, Tent-SSA-BPNN significantly improves stability, achieving (ACC) 96.218%, root mean square error (RMSE) 0.952, coefficient determination (R2) 0.9939, surpassing previous models. results show that (1) from 2010 2022, average JHP fluctuated within specific range, exhibiting decrease 0.69%, significant differences distributions across various cities; (2) was (R²) value 0.9939. (3) those preoptimization model, ACC, RMSE, R² values terms clearly indicating efficacy optimization. (4) reveal proportion consumption has impact on AWUE. These provide actionable insights optimizing resource allocation, particularly water-scarce regions, guide policymakers management strategies, supporting development.

Language: Английский

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A multi-strategy enhanced African vultures optimization algorithm for global optimization problems

Journal of Computational Design and Engineering, Journal Year: 2022, Volume and Issue: 10(1), P. 329 - 356

Published: Dec. 14, 2022

Abstract The African vultures optimization algorithm (AVOA) is a recently proposed metaheuristic inspired by the vultures’ behaviors. Though basic AVOA performs very well for most problems, it still suffers from shortcomings of slow convergence rate and local optimal stagnation when solving complex tasks. Therefore, this study introduces modified version named enhanced (EAVOA). EAVOA uses three different techniques namely representative vulture selection strategy, rotating flight selecting accumulation mechanism, respectively, which are developed based on AVOA. strategy strikes good balance between global searches. mechanism utilized to improve quality solution. performance validated 23 classical benchmark functions with various types dimensions compared those nine other state-of-the-art methods according numerical results curves. In addition, real-world engineering design problems adopted evaluate practical applicability EAVOA. Furthermore, has been applied classify multi-layer perception using XOR cancer datasets. experimental clearly show that superiority over methods.

Language: Английский

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Improved African Vulture Optimization Algorithm Based on Quasi-Oppositional Differential Evolution Operator

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 95197 - 95218

Published: Jan. 1, 2022

In this study, an improved African vulture optimization algorithm (IAVOA) that combines the (AVOA) with both quasi-oppositional learning and differential evolution is proposed to address specific drawbacks of AVOA, including low population diversity, bad development capability, unbalanced exploration capabilities. The has three parts. First, introduced in initialization stages improve diversity. Second, a operator local search position update each capability. Third, adaptive parameters are operator, thus balancing development. A numerical simulation experiment based on 36 different types benchmark functions showed IAVOA can enhance convergence speed solution accuracy basic AVOA two variants while exhibiting superior performance compared those other swarm intelligence algorithms.

Language: Английский

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Research on diagnosis method of centrifugal pump rotor faults based on IPSO-VMD and RVM

Nuclear Engineering and Technology, Journal Year: 2022, Volume and Issue: 55(3), P. 827 - 838

Published: Nov. 5, 2022

Centrifugal pump is a key part of nuclear power plant systems, and its health status critical to the safety reliability plants. Therefore, fault diagnosis required for centrifugal pump. Traditional methods have difficulty extracting features from nonlinear non-stationary signals, resulting in low diagnostic accuracy. In this paper, new method proposed based on improved particle swarm optimization (IPSO) algorithm-based variational modal decomposition (VMD) relevance vector machine (RVM). Firstly, simulation test bench rotor faults built, which vibration displacement signals are also collected by eddy current sensors. Then, algorithm used optimize VMD achieve adaptive signals. Meanwhile, screening criterion minimum Kullback-Leibler (K-L) divergence value established extract primary intrinsic function (IMF) component. Eventually, factors obtained IMF component form feature vector, patterns recognized using RVM model. The results show that extraction information classification been improved, average accuracy could reach 97.87%.

Language: Английский

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