Developed NSGA-II to Solve Multi Objective Optimization Models in WSNs

Hasson, Saad Talib; Khudhair, Hayder Ayad

doi:10.1109/icoase.2018.8548860

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…MOO is important because it can model real-world problems with multiple conflicting objectives [59,60]. To address the various challenges in the building energy sector, energy optimization applying MOO is essential to meet users' needs while also reducing technical issues and energy usage in the building energy sector.…”

Section: Energy Optimization In Buildingsmentioning

confidence: 99%

A Review of Methodologies for Managing Energy Flexibility Resources in Buildings

Pedram,

Asadi,

Chenari

et al. 2023

Energies

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The integration of renewable energy and flexible energy sources in buildings brings numerous benefits. However, the integration of new technologies has increased the complexity and despite the progress of optimization algorithms and technologies, new research challenges emerge. With the increasing availability of data and advanced modeling tools, stakeholders in the building sector are actively seeking a more comprehensive understanding of the implementation and potential benefits of energy optimization and an extensive up-to-date survey of optimization in the context of buildings and communities is missing in the literature. This study comprehensively reviews over 180 relevant publications on the management and optimization of energy flexibility resources in buildings. The primary objective was to examine and analyze prior research, with emphasis on the used methods, objectives, and scope. The method of content analysis was used to ensure a thorough examination of the existing literature on the subject. It was concluded that multi-objective optimization is crucial to enhance the utilization of flexible resources within individual buildings and communities. Moreover, the study successfully pinpointed key challenges and opportunities for future research, such as the need for accurate data, the complexity of the optimization process, and the potential trade-offs between different objectives.

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Section: Energy Optimization In Buildingsmentioning

confidence: 99%

A Review of Methodologies for Managing Energy Flexibility Resources in Buildings

Pedram,

Asadi,

Chenari

et al. 2023

Energies

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“…Le Berre et al 33 proposed a modified NSGA-II of bi-objective sensor placement with the minimization of the number of deployed sensors and the minimization of the tracking constraints violations, under the coverage constraints. In Hasson and Khudhair’s 34 study, NSGA-II is used recently as a tool to obtain the best trade-off between network coverage and connectivity as two competing objectives. Harizan and Kuila 35 proposed a novel NSGA-II with modified dominance to solve the multi-objective coverage and connectivity problem.…”

Section: Related Workmentioning

confidence: 99%

Sensor placement optimization for critical-grid coverage problem of indoor positioning

Liu

Yin

2020

International Journal of Distributed Sensor Networks

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It is more practical and efficient to deploy sensors in critical areas rather than common areas to ensure indoor positioning accuracy and reduce deployment cost. This study focused on the sensor placement optimization for critical-grid coverage problem with two objectives: accuracy and cost. After reviewing some related works, this article proposed a multi-objective optimization model for critical-grid coverage problem of indoor positioning considering k-coverage problem as well as the topological rationality of sensor distribution. Then, NSGA-II algorithm was used to solve the optimizing model of sensor placement. At last, the simulation experiment and real environment validation were conducted for proposed method. The results showed that the optimized schemes obtain a lower error (1.13, 1.21 m) and a higher reduction of sensor deployment cost than the uniform deployment scheme (1.44 m). As a conclusion, the proposed method could reduce the cost of sensor deployment while ensuring the accuracy of indoor positioning for critical areas. It also provides a new direction for improving the accuracy of indoor positioning.

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DSNs Coverage Optimization Based on Improved Multiobjective Army Ant Search Optimizer

Yao,

Zhao,

Wen

et al. 2024

IEEE Sensors J.

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Developed NSGA-II to Solve Multi Objective Optimization Models in WSNs

Cited by 4 publications

References 13 publications

A Review of Methodologies for Managing Energy Flexibility Resources in Buildings

A Review of Methodologies for Managing Energy Flexibility Resources in Buildings

Sensor placement optimization for critical-grid coverage problem of indoor positioning

DSNs Coverage Optimization Based on Improved Multiobjective Army Ant Search Optimizer

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