Adaptive Tasmanian Devil Optimizer for Global Optimization and Application in Wireless Sensor Network Deployment

IEEE Access, Год журнала: 2024, Номер 12, С. 72382 - 72407

Опубликована: Янв. 1, 2024

The Tasmanian Devil Optimization (TDO) algorithm, a recently popular metaheuristic exhibits issues such as slow convergence, low precision, and susceptibility to getting stuck in local optima when applied practical problems. In order address these drawbacks, we propose an improved version called the Adaptive Optimizer (ATDO). enhancements, introduce best point set population initialization enhance ATDO's exploration capabilities. adoption of adaptive Levy flight strategy improves convergence precision speed. introduction Brownian random walk effectively prevents problem being trapped optimal solutions. Additionally, use cosine-type change-based differential evolution algorithm's crossover enhances overall quality. To validate performance ATDO, detailed analysis is conducted terms diversity, exploration-exploitation balance, behavior. Furthermore, apply ATDO CEC-2017 for global optimization evaluation. Results from Wilcoxon rank-sum Friedman average rank tests demonstrate that, compared TDO other algorithms, superior comprehensive performance. Finally, wireless sensor network layout two engineering problems, experimental results show that outperforms comparative further validating its scalability excellence.

Язык: Английский

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A multi-strategy improved rime optimization algorithm for three-dimensional USV path planning and global optimization

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

Опубликована: Июнь 1, 2024

The RIME optimization algorithm (RIME) represents an advanced technique. However, it suffers from issues such as slow convergence speed and susceptibility to falling into local optima. In response these shortcomings, we propose a multi-strategy enhanced version known the improved (MIRIME). Firstly, Tent chaotic map is utilized initialize population, laying groundwork for global optimization. Secondly, introduce adaptive update strategy based on leadership dynamic centroid, facilitating swarm's exploitation in more favorable direction. To address problem of population scarcity later iterations, lens imaging opposition-based learning control introduced enhance diversity ensure accuracy. proposed centroid boundary not only limits search boundaries individuals but also effectively enhances algorithm's focus efficiency. Finally, demonstrate performance MIRIME, employ CEC 2017 2022 test suites compare with 11 popular algorithms across different dimensions, verifying its effectiveness. Additionally, assess method's practical feasibility, apply MIRIME solve three-dimensional path planning unmanned surface vehicles. Experimental results indicate that outperforms other competing terms solution quality stability, highlighting superior application potential.

Язык: Английский

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Advancing bankruptcy prediction: a study on an improved rime optimization algorithm and its application in feature selection

International Journal of Machine Learning and Cybernetics, Год журнала: 2025, Номер unknown

Опубликована: Янв. 24, 2025

Язык: Английский

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A boosted African vultures optimization algorithm combined with logarithmic weight inspired novel dynamic chaotic opposite learning strategy

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126532 - 126532

Опубликована: Янв. 1, 2025

Язык: Английский

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Probabilistic luminance estimation and optimized gamma correction for Wireless capsule endoscopy

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 104, С. 107558 - 107558

Опубликована: Фев. 6, 2025

Язык: Английский

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