Genghis Khan shark optimizer: A novel nature-inspired algorithm for engineering optimization

Advanced Engineering Informatics, Год журнала: 2023, Номер 58, С. 102210 - 102210

Опубликована: Окт. 1, 2023

Язык: Английский

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Electric eel foraging optimization: A new bio-inspired optimizer for engineering applications

Expert Systems with Applications, Год журнала: 2023, Номер 238, С. 122200 - 122200

Опубликована: Окт. 23, 2023

Язык: Английский

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Agricultural UAV trajectory planning by incorporating multi-mechanism improved grey wolf optimization algorithm

Expert Systems with Applications, Год журнала: 2023, Номер 233, С. 120946 - 120946

Опубликована: Июль 6, 2023

Язык: Английский

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Leveraging opposition-based learning for solar photovoltaic model parameter estimation with exponential distribution optimization algorithm

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 4, 2024

Abstract Given the multi-model and nonlinear characteristics of photovoltaic (PV) models, parameter extraction presents a challenging problem. This challenge is exacerbated by propensity conventional algorithms to get trapped in local optima due complex nature Accurate estimation, nonetheless, crucial its significant impact on PV system’s performance, influencing both current energy production. While traditional methods have provided reasonable results for model variables, they often require extensive computational resources, which impacts precision robustness many fitness evaluations. To address this problem, paper an improved algorithm extraction, leveraging opposition-based exponential distribution optimizer (OBEDO). The OBEDO method, equipped with learning, provides enhanced exploration capability efficient exploitation search space, helping mitigate risk entrapment optima. proposed rigorously verified against state-of-the-art across various including single-diode, double-diode, three-diode, module models. Practical statistical reveal that performs better than other estimating parameters, demonstrating superior convergence speed, reliability, accuracy. Moreover, performance assessed using several case studies, further reinforcing effectiveness. Therefore, OBEDO, advantages terms efficiency robustness, emerges as promising solution identification, making contribution enhancing systems.

Язык: Английский

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Enhanced multi-strategy bottlenose dolphin optimizer for UAVs path planning

Applied Mathematical Modelling, Год журнала: 2024, Номер 130, С. 243 - 271

Опубликована: Март 11, 2024

Язык: Английский

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Optimal model parameter estimation and performance analysis of PEM electrolyzer using modified honey badger algorithm

International Journal of Hydrogen Energy, Год журнала: 2023, Номер 49, С. 238 - 259

Опубликована: Авг. 2, 2023

Язык: Английский

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A novel state transition algorithm with adaptive fuzzy penalty for multi-constraint UAV path planning

Expert Systems with Applications, Год журнала: 2024, Номер 248, С. 123481 - 123481

Опубликована: Фев. 12, 2024

Язык: Английский

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Orthogonal opposition-based learning honey badger algorithm with differential evolution for global optimization and engineering design problems

Alexandria Engineering Journal, Год журнала: 2024, Номер 91, С. 348 - 367

Опубликована: Фев. 19, 2024

Honey badger algorithm (HBA) is a recent swarm-based metaheuristic that excels in simplicity and high exploitation capability. However, it suffers from some limitations including weak exploration capacity an imbalance between exploitation. In this paper, improved honey called ODEHBA proposed to improve the performance of basic HBA. Firstly, orthogonal opposition-based learning technique employed assist population escaping local optimum. Secondly, differential evolution utilized ensure enrichment diversity enhance convergence speed. Finally, capability boosted by equilibrium pool strategy. To validate efficacy ODEHBA, compared with 13 well-known algorithms on CEC2022 benchmark test sets. Friedman Wilcoxon rank-sum are assess ODEHBA. Furthermore, three engineering design problems Internet Vehicles (IoV) routing problem applied The simulation results demonstrate solving complex numerical problems, design, IoV problems. This holds significant practical implications for cost reduction resource utilization.

Язык: Английский

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Improved Dung Beetle Optimizer Algorithm With Multi-Strategy for Global Optimization and UAV 3D Path Planning

IEEE Access, Год журнала: 2024, Номер 12, С. 69240 - 69257

Опубликована: Янв. 1, 2024

In high-dimensional scenarios, path planning is a challenging and computationally complex optimization task that requires finding optimal paths within domains. Metaheuristic (MH) algorithms offer practical approach to addressing this issue. The Dung Beetle Optimizer (DBO), categorized as MH algorithm, takes inspiration from the biological behaviors exhibited by dung beetles. However, DBO exhibits limitations, including inadequate global search capabilities tendency converge on local optima. To address these challenges, paper proposes multi-strategy Improved Optimization algorithm (IDBO) for UAV 3D planning. Initially, cubic chaos mapping applied population initialization, enhancing diversity. Subsequently, novel exploration strategy replaces DBO's original rolling phase, improving information exchange minimizing parameter dependence. Third, an adaptive t-distribution introduced adjust beetle positions, balancing exploitation. Finally, enhanced update proposed, utilizing varied behavioral logic at different stages improve solution quality efficiency. Additionally, performance comparisons with six advanced CEC2017 test suite, validation of IDBO's effectiveness via Wilcoxon rank-sum Friedman mean rank test. Meanwhile, in experiment, IDBO achieves best cost index, which 1.34% higher than DBO, also significantly better most such WOA, GSA, HHO, COA, standard deviation reduced 99.93% compared proves robustness

Язык: Английский

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SDO: A novel sled dog-inspired optimizer for solving engineering problems

Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102783 - 102783

Опубликована: Авг. 28, 2024

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