An Improved Design of Knee Orthosis Using Self-Adaptive Bonobo Optimizer (SaBO)

Das, Amit Kumar; Sahoo, Saikat; Pratihar, Dilip Kumar

doi:10.1007/s10846-022-01802-1

Cited by 9 publications

(2 citation statements)

References 32 publications

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“…In addition, SABO has three additional memorized populations that are distinct from the current population, and the members of these populations mate to give birth to modern bonobos. The SABO's controlling criteria are based on a repulsion-based learning approach [30]. Other than these, 𝑝 𝑡ℎ -bonobo choice in SABO is planned extraordinarily.…”

Section: Self-adaptive Bonobo Optimizer (Sabo)mentioning

confidence: 99%

Enhancing Building Energy Efficiency: A Hybrid Meta-Heuristic Approach for Cooling Load Prediction

Wang,

Zhou,

Deng

et al. 2024

IJACSA

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The research tackles the complex problem of accurately predicting cooling loads in the context of energy efficiency and building management. It presents a novel approach that increases the precision of cooling load forecasts by utilizing machine learning (ML). The main objective is to incorporate a hybridization strategy into Radial Basis Function (RBF) models, a commonly used method for cooling load prediction, to improve their effectiveness. This new method significantly increases accuracy and reliability. The resulting hybrid models, which combine two powerful optimization techniques, outperform the state-of-the-art approaches and mark a major advancement in predictive modelling. The study performs in-depth analyses to compare standalone and hybrid model configurations, guaranteeing an unbiased and thorough performance evaluation. The deliberate choice of incorporating the Self-adaptive Bonobo Optimizer (SABO) and Differential Squirrel Search Algorithm (DSSA) underscores the significance of leveraging the distinctive strengths of each optimizer. The study delves into three variations of the RBF model: RBF, RBDS, and RRBSA. Among these, the RBF model, integrating the SABO optimizer (RBSA), distinguishes itself with an impressive R 2 value of 0.995, denoting an exceptionally close alignment with the data. Furthermore, a low Root Mean Square Error (RMSE) value of 0.700 underscores the model's remarkable precision. The research showcases the effectiveness of fusing ML techniques in the RBSA model for precise cooling load predictions. This hybrid model furnishes more dependable insights for energy conservation and sustainable building operations, thereby contributing to a more environmentally conscious and sustainable future.

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Section: Self-adaptive Bonobo Optimizer (Sabo)mentioning

confidence: 99%

Enhancing Building Energy Efficiency: A Hybrid Meta-Heuristic Approach for Cooling Load Prediction

Wang,

Zhou,

Deng

et al. 2024

IJACSA

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“…Addressing this, a metaheuristic optimization algorithm, namely, Bonobo optimizer (BO) was introduced. Inspired by the social and reproductive behaviors of bonobos, BO offers a unique approach for tackling optimization problems. − The application of the BO algorithm to various optimization problems in engineering sciences unveiled its robustness and efficiency compared to existing algorithms. Intriguingly, this sophisticated BO algorithm has remained uncharted territory in the realm of chemical problem solving, offering a compelling opportunity for exploration.…”

Section: Introductionmentioning

confidence: 99%

Bonobo Optimizer: A New Tool Toward the Global Optimization of Small Atomic Clusters

Chakraborty,

Pal,

Pratihar

et al. 2024

J. Phys. Chem. A

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In the realm of structural and bonding investigations within chemical systems, elucidating global minimum energy configurations stands as a paramount goal. As the systems increase in size and complexity, this pursuit becomes progressively challenging. Herein, we introduce Bonobo optimizer (BO), a metaheuristic algorithm inspired by the social and reproductive behaviors of bonobos, to the domain of chemical problem solving. Focusing on small carbon clusters, this study systematically evaluates BO's performance, showcasing its robustness and efficiency. Parametric studies highlight the algorithm's adaptability, consistently converging to global minimum structures. Rigorous statistical validation supports the results, and a comparative analysis against established global optimization algorithms underlines BO's superior efficiency. This exploration extends the applicability of BO to the optimization of atomic clusters, providing a promising avenue for future advancements in computational chemistry.

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Exploring the Possibility of a Planar Tetracoordinate Boron in BXY₃ (X = B, Al, Ga; Y = C, Si, Ge) Clusters: A Theoretical Study

Chakraborty,

Chattaraj

2024

J Comput Chem

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In this study, we investigated the potential energy surface of BXY3 (X = B, Al, Ga; Y = C, Si, Ge) clusters employing a few global optimization techniques. Remarkably, the global minimum structure obtained for most of the cases revealed a planar tetracoordinate boron atom, shedding light on the inherent stability of this motif. A comparative analysis of the performance of the different global optimization techniques employed is presented, offering insights into their efficacy. Additionally, the overall stability of the obtained global minimum structures is thoroughly examined through Atom‐centered Density Matrix Propagation (ADMP) simulations spanning 20 ps at temperatures 300 and 500 K. The aromaticity of the respective clusters is also assessed via Nucleus Independent Chemical Shift (NICS) and Isochemical Shielding Surface (ICSS) calculations, providing valuable information regarding their electronic structure and stability. This comprehensive theoretical investigation contributes to our understanding of the structural properties of these clusters, with implications for their potential applications in various fields of chemistry.

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An Improved Design of Knee Orthosis Using Self-Adaptive Bonobo Optimizer (SaBO)

Cited by 9 publications

References 32 publications

Enhancing Building Energy Efficiency: A Hybrid Meta-Heuristic Approach for Cooling Load Prediction

Enhancing Building Energy Efficiency: A Hybrid Meta-Heuristic Approach for Cooling Load Prediction

Bonobo Optimizer: A New Tool Toward the Global Optimization of Small Atomic Clusters

Exploring the Possibility of a Planar Tetracoordinate Boron in BXY₃ (X = B, Al, Ga; Y = C, Si, Ge) Clusters: A Theoretical Study

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An Improved Design of Knee Orthosis Using Self-Adaptive Bonobo Optimizer (SaBO)

Cited by 9 publications

References 32 publications

Enhancing Building Energy Efficiency: A Hybrid Meta-Heuristic Approach for Cooling Load Prediction

Enhancing Building Energy Efficiency: A Hybrid Meta-Heuristic Approach for Cooling Load Prediction

Bonobo Optimizer: A New Tool Toward the Global Optimization of Small Atomic Clusters

Exploring the Possibility of a Planar Tetracoordinate Boron in BXY3 (X = B, Al, Ga; Y = C, Si, Ge) Clusters: A Theoretical Study

Contact Info

Product

Resources

About

Exploring the Possibility of a Planar Tetracoordinate Boron in BXY₃ (X = B, Al, Ga; Y = C, Si, Ge) Clusters: A Theoretical Study