IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i21p2725-d666127.html
   My bibliography  Save this article

Feasible Optimal Solutions of Electromagnetic Cloaking Problems by Chaotic Accelerated Particle Swarm Optimization

Author

Listed:
  • Alkmini Michaloglou

    (School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
    These authors contributed equally to this work.)

  • Nikolaos L. Tsitsas

    (School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
    These authors contributed equally to this work.)

Abstract

The optimization problem of cloaking a perfectly electric conducting or dielectric spherical core is investigated. The primary excitation is due to an external magnetic dipole. The chaotic accelerated particle swarm optimization (CAPSO) algorithm is adjusted and applied to this optimization problem. The optimization variables are the radii, the permittivities and the permeabilities of a small number of spherical shells covering the core. Several feasible optimal designs are obtained, which exhibit perfect or almost perfect cloaking performance for all angles of observation. These optimal designs correspond to two, three or four spherical coating layers composed of ordinary materials. Detailed parametric investigations of the cloaking mechanism with respect to the type and radius of the core and the location of the primary dipole are carried out. The presented optimization procedure and the reported results are expected to be useful in applications like scattering and characterization of optical particles as well as in designing low-profile receiving antennas.

Suggested Citation

  • Alkmini Michaloglou & Nikolaos L. Tsitsas, 2021. "Feasible Optimal Solutions of Electromagnetic Cloaking Problems by Chaotic Accelerated Particle Swarm Optimization," Mathematics, MDPI, vol. 9(21), pages 1-23, October.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2725-:d:666127
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/21/2725/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/21/2725/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Abdou, Ahmed Fathi, 2019. "Modified PSO algorithm for real-time energy management in grid-connected microgrids," Renewable Energy, Elsevier, vol. 136(C), pages 746-757.
    2. Sotirios K. Goudos & Zaharias D. Zaharis & Konstantinos B. Baltzis, 2018. "Particle Swarm Optimization as Applied to Electromagnetic Design Problems," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 9(2), pages 47-82, April.
    3. Ajdad, H. & Filali Baba, Y. & Al Mers, A. & Merroun, O. & Bouatem, A. & Boutammachte, N., 2019. "Particle swarm optimization algorithm for optical-geometric optimization of linear fresnel solar concentrators," Renewable Energy, Elsevier, vol. 130(C), pages 992-1001.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ovidiu Ivanov & Samiran Chattopadhyay & Soumya Banerjee & Bogdan-Constantin Neagu & Gheorghe Grigoras & Mihai Gavrilas, 2020. "A Novel Algorithm with Multiple Consumer Demand Response Priorities in Residential Unbalanced LV Electricity Distribution Networks," Mathematics, MDPI, vol. 8(8), pages 1-24, July.
    2. Muhammad Umair Safder & Mohammad J. Sanjari & Ameer Hamza & Rasoul Garmabdari & Md. Alamgir Hossain & Junwei Lu, 2023. "Enhancing Microgrid Stability and Energy Management: Techniques, Challenges, and Future Directions," Energies, MDPI, vol. 16(18), pages 1-28, September.
    3. Qunpeng Fan, 2022. "Management and Policy Modeling of the Market Using Artificial Intelligence," Sustainability, MDPI, vol. 14(14), pages 1-14, July.
    4. Ramin Nourollahi & Pouya Salyani & Kazem Zare & Behnam Mohammadi-Ivatloo & Zulkurnain Abdul-Malek, 2022. "Peak-Load Management of Distribution Network Using Conservation Voltage Reduction and Dynamic Thermal Rating," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    5. Ji-Won Lee & Mun-Kyeom Kim & Hyung-Joon Kim, 2021. "A Multi-Agent Based Optimization Model for Microgrid Operation with Hybrid Method Using Game Theory Strategy," Energies, MDPI, vol. 14(3), pages 1-21, January.
    6. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    7. Zhang, Li & Gao, Yan & Zhu, Hongbo & Tao, Li, 2022. "Bi-level stochastic real-time pricing model in multi-energy generation system: A reinforcement learning approach," Energy, Elsevier, vol. 239(PA).
    8. e Silva, Danilo P. & Félix Salles, José L. & Fardin, Jussara F. & Rocha Pereira, Maxsuel M., 2020. "Management of an island and grid-connected microgrid using hybrid economic model predictive control with weather data," Applied Energy, Elsevier, vol. 278(C).
    9. Prakash, K. & Ali, M. & Hossain, M A & Kumar, Nallapaneni Manoj & Islam, M.R. & Macana, C.A. & Chopra, Shauhrat S. & Pota, H.R., 2022. "Planning battery energy storage system in line with grid support parameters enables circular economy aligned ancillary services in low voltage networks," Renewable Energy, Elsevier, vol. 201(P1), pages 802-820.
    10. Brandon Cortés-Caicedo & Luis Fernando Grisales-Noreña & Oscar Danilo Montoya & Miguel Angel Rodriguez-Cabal & Javier Alveiro Rosero, 2022. "Energy Management System for the Optimal Operation of PV Generators in Distribution Systems Using the Antlion Optimizer: A Colombian Urban and Rural Case Study," Sustainability, MDPI, vol. 14(23), pages 1-35, December.
    11. Abdelfettah Kerboua & Fouad Boukli-Hacene & Khaldoon A Mourad, 2020. "Particle Swarm Optimization for Micro-Grid Power Management and Load Scheduling," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 71-80.
    12. Aslani, Mehrdad & Mashayekhi, Mehdi & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2022. "Robust optimal operation of energy hub incorporating integrated thermal and electrical demand response programs under various electric vehicle charging modes," Applied Energy, Elsevier, vol. 321(C).
    13. Li, Shenglin & Zhu, Jizhong & Chen, Ziyu & Luo, Tengyan, 2021. "Double-layer energy management system based on energy sharing cloud for virtual residential microgrid," Applied Energy, Elsevier, vol. 282(PA).
    14. Pinciroli, Luca & Baraldi, Piero & Compare, Michele & Zio, Enrico, 2023. "Optimal operation and maintenance of energy storage systems in grid-connected microgrids by deep reinforcement learning," Applied Energy, Elsevier, vol. 352(C).
    15. Nemanja Mišljenović & Matej Žnidarec & Goran Knežević & Damir Šljivac & Andreas Sumper, 2023. "A Review of Energy Management Systems and Organizational Structures of Prosumers," Energies, MDPI, vol. 16(7), pages 1-32, March.
    16. Cheng, Shen & Zhao, Gaiju & Gao, Ming & Shi, Yuetao & Huang, Mingming & Yousefi, Nasser, 2021. "Optimal hybrid energy system for locomotive utilizing improved Locust Swarm optimizer," Energy, Elsevier, vol. 218(C).
    17. Dadashi-Rad, Mohammad Hosein & Ghasemi-Marzbali, Ali & Ahangar, Roya Ahmadi, 2020. "Modeling and planning of smart buildings energy in power system considering demand response," Energy, Elsevier, vol. 213(C).
    18. Raya-Armenta, Jose Maurilio & Bazmohammadi, Najmeh & Avina-Cervantes, Juan Gabriel & Sáez, Doris & Vasquez, Juan C. & Guerrero, Josep M., 2021. "Energy management system optimization in islanded microgrids: An overview and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    19. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    20. Ghazouani, Mokhtar & Bouya, Mohsine & Benaissa, Mohammed, 2020. "Thermo-economic and exergy analysis and optimization of small PTC collectors for solar heat integration in industrial processes," Renewable Energy, Elsevier, vol. 152(C), pages 984-998.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2725-:d:666127. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.