Experimental validation of an AI-embedded FPGA-based Real-Time smart energy management system using Multi-Objective Reptile search algorithm and gorilla troops optimizer

Elkholy, M.H.; Elymany, Mahmoud; Yona, Atsushi; Senjyu, Tomonobu; Lotfy, Mohammed Elsayed

doi:10.1016/j.enconman.2023.116860

Cited by 18 publications

(4 citation statements)

References 70 publications

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“…The case study's findings and analysis are presented in the parts that follow, which are followed by a discussion of the ramifications and real-world uses of analytics and IoT for waste management optimization. The report concludes with a strong summary of the research results and highlights the potential of data-driven techniques to transform waste management methods, improving resource utilization and environmental sustainability in urban settings [16]- [19].…”

Section: Goals Of the Researchmentioning

confidence: 98%

Optimizing Waste Management through IoT and Analytics: A Case Study Using the Waste Management Optimization Test

Kuzhin,

Joshi,

Mittal

et al. 2024

BIO Web Conf.

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This research examines how Internet of Things (IoT) technology and advanced analytics may be integrated into trash management. The results show a notable improvement in waste collection efficiency, cost savings, and environmental sustainability. Significant operational cost reductions were achieved by reducing the number of overfilled trash cans by 20% and the frequency of collections by 15% as a consequence of real-time data capture using IoT sensors. Additionally, a 25% reduction in trip distance was made possible by data-driven route optimization, which also resulted in a 10% drop in fuel use and a decrease in carbon emissions. The data-driven strategy also found areas for recycling, which increased the amount of recyclables collected by 15%. These findings highlight the promise that data-driven trash management has for improving both environmental and economic sustainability while tackling the problems associated with urban garbage.

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Section: Goals Of the Researchmentioning

confidence: 98%

Optimizing Waste Management through IoT and Analytics: A Case Study Using the Waste Management Optimization Test

Kuzhin,

Joshi,

Mittal

et al. 2024

BIO Web Conf.

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“…The Algorithm exhibits versatility as it can address multiple issues, including unconstrained and constrained problems, single-objective and multi-objective problems, and difficulties involving continuous and discrete variables. Many fields, including finance, image, and signal processing [13], engineering [14], renewable energy [15], machine learning, and many others, have adopted it due to its exceptional efficiency and robustness. This is because it outperforms other popular optimization methods, has a sufficiently fast execution time, and has a good quality convergence rate.…”

Section: Introductionmentioning

confidence: 99%

Reptile Search Algorithm for Association Rule Mining

Boukhalat,

Heraguemi,

Benouis

et al. 2024

IJCDS

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Association rule mining (ARM) is a very popular, engaging, and active research area in data mining. It seeks to find valuable connections between different attributes in a defined dataset. ARM, which describes it as an NP-complete problem, creates a fertile field for optimization applications. The Reptile Search Algorithm (RSA) is an innovative evolutionary algorithm. It yanks stimulation from the encircling and hunting conducts of crocodiles. It is a well-known optimization technique for solving NP-complete issues. Since its introduction by Abualigah et al. in 2022, the approach has attracted considerable attention from researchers and has extensively been used to address diverse optimization issues in several disciplines. This is due to its satisfactory execution speed, efficient convergence rate, and superior effectiveness compared to other widely recognized optimization methods. This paper suggests a new version of the reptile search algorithm for resolving the association rules mining challenge. Our proposal inherits the trade-off between local and global search optimization issues demonstrated by the Reptile search algorithm. To illustrate the power of our proposal, a sequence of experiments is taken out on a varied, well-known, employing multiple comparison criteria. The results show an evident dominance of the proposed approach in the front of the famous association rules mining algorithms as well as Bees Swarm Optimization (BSO), Bat Algorithm (BA), Whale Optimization Algorithm (WOA), and others regarding CPU time, fitness criteria, and the quality of generated rules.

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“…Xiong et al proposed a dual-scale deep learning model based on ELM-BiLSTM and improved the reptile search algorithm for wind power prediction [ 28 ]. Elkholy et al proposed an AI-embedded FPGA-based real-time intelligent energy management system using a multi-objective reptile search algorithm and a gorilla troops optimizer [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Reptile Search Algorithm Considering Different Flight Heights to Solve Engineering Optimization Design Problems

Yao

Yuan

et al. 2023

Biomimetics

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The reptile search algorithm is an effective optimization method based on the natural laws of the biological world. By restoring and simulating the hunting process of reptiles, good optimization results can be achieved. However, due to the limitations of natural laws, it is easy to fall into local optima during the exploration phase. Inspired by the different search fields of biological organisms with varying flight heights, this paper proposes a reptile search algorithm considering different flight heights. In the exploration phase, introducing the different flight altitude abilities of two animals, the northern goshawk and the African vulture, enables reptiles to have better search horizons, improve their global search ability, and reduce the probability of falling into local optima during the exploration phase. A novel dynamic factor (DF) is proposed in the exploitation phase to improve the algorithm’s convergence speed and optimization accuracy. To verify the effectiveness of the proposed algorithm, the test results were compared with ten state-of-the-art (SOTA) algorithms on thirty-three famous test functions. The experimental results show that the proposed algorithm has good performance. In addition, the proposed algorithm and ten SOTA algorithms were applied to three micromachine practical engineering problems, and the experimental results show that the proposed algorithm has good problem-solving ability.

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Experimental validation of an AI-embedded FPGA-based Real-Time smart energy management system using Multi-Objective Reptile search algorithm and gorilla troops optimizer

Cited by 18 publications

References 70 publications

Optimizing Waste Management through IoT and Analytics: A Case Study Using the Waste Management Optimization Test

Optimizing Waste Management through IoT and Analytics: A Case Study Using the Waste Management Optimization Test

Reptile Search Algorithm for Association Rule Mining

Reptile Search Algorithm Considering Different Flight Heights to Solve Engineering Optimization Design Problems

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