Modified blind naked mole-rat algorithm applied to electromagnetic design problems

• Autorzy: Taherdangkoo Mohammad

Identyfikatory

DOI

10.24425/aee.2021.136990

• Języki publikacji: EN

Abstrakty

EN

In this paper,we proposed a modified meta-heuristic algorithm based on the blind naked mole-rat (BNMR) algorithm to solve the multiple standard benchmark problems. We then apply the proposed algorithm to solve an engineering inverse problem in the electromagnetic field to validate the results. The main objective is to modify the BNMR algorithm by employing two different types of distribution processes to improve the search strategy. Furthermore, we proposed an improvement scheme for the objective function and we have changed some parameters in the implementation of the BNMR algorithm. The performance of the BNMR algorithm was improved by introducing several new parameters to find the better target resources in the implementation of a modified BNMR algorithm. The results demonstrate that the changed candidate solutions fall into the neighborhood of the real solution. The results show the superiority of the propose method over other methods in solving various mathematical and electromagnetic problems.

Słowa kluczowe

EN

electromagnetic design problems; global optimization; meta-heuristic algorithm

PL

problemy z projektowaniem elektromagnetycznym; globalna optymalizacja; algorytm metaheurystyczny

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2021
• Tom: Vol. 70, nr 2
• Strony: 167--380
• Opis fizyczny: Bibliogr. 16 poz., rys., wz., tab.

Twórcy

autor

Taherdangkoo Mohammad

• Institute of Research and Development, Duy Tan University Da Nang 550000, Vietnam

• https://orcid.org/0000-0003-0722-574X

Bibliografia

• [1] Taherdangkoo M., Modified stem cells algorithm for Loney’s solenoid benchmark problem, International Journal Applied Electromagnetics and Mechanics, vol. 42, no. 3, pp. 437–445 (2013).
• [2] Coelho L.D.S., Alotto P., Loney’s Solenoid Design Using an Artificial Immune Network with Local Search Based on the Simplex Method, IEEE Transactions on Magnetics, vol. 44, no. 6, pp. 1070–1073 (2008).
• [3] Khan T.A., Sai Ho Ling, An improved gravitational search algorithm for solving an electromagnetic design problem, Journal of Computational Electronics, vol. 19, no. 2, pp. 773–779 (2020), DOI: 10.1007/s10825-020-01476-8.
• [4] Duca A., Ciuprina G., Lup S., Hameed I. ,ACORalgorithm’s efficiency for electromagnetic optimization benchmark problems, International Symposium on Advanced Topics in Electrical Engineering, pp. 1–5 (2019).
• [5] Coelho L.D.S., Alotto P., Gaussian Artificial Bee Colony Algorithm Approach Applied to Loney’s Solenoid Benchmark Problem, IEEE Transactions on Magnetics, vol. 47, no. 5, pp. 1326–1329 (2011).
• [6] Duca A., Duca L., Ciuprina G., QPSO with avoidance behavior to solve electromagnetic optimization problems, International Journal of Applied Electromagnetics and Mechanics, vol. 1, pp. 1–7 (2018).
• [7] Coelho L.D.S., Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems, Expert Systems with Applications, vol. 37, pp. 1676–1683 (2010).
• [8] Ciuprina G., Ioan D., Munteanu I., Use of Intelligent-particle swarm optimization in electromagnetic, IEEE Transactions on Magnetics, vol. 38, no. 2, pp. 1037–1040 (2002).
• [9] Rehman O., Yang Sh., Khan Sh., Rahman S., A quantum particle swarm optimizer with enhanced strategy for global optimization of electromagnetic devices, IEEE Transactions on Magnetics, vol. 55, no. 8 (2019), DOI: 10.1109/TMAG.2019.2913021.
• [10] Taherdangkoo M., Shirzadi M.H., Yazdi M., Bagheri M.H., A robust clustering method based on blind, naked molerats (BNMR) algorithm, Swarm and Evolutionary Computation, vol. 10, pp. 1–11 (2013).
• [11] Taherdangkoo M., Taherdangkoo M., Modified BNMR algorithm applied to Loney’s solenoid benchmark problem, International Journal of Applied Electromagnetics and Mechanics, vol. 46, no. 3, pp. 683–692 (2014).
• [12] Taherdangkoo M., Shirzadi M. H., Bagheri M. H., A novel meta-heuristic algorithm for numerical function optimization: Blind, naked molerats (BNMR) algorithm, Scientific Research and Essays, vol. 7, no. 41, pp. 3566–3583 (2012).
• [13] Suganthan P. N., Hansen N., Liang J. J., Deb K., Chen Y. P., Auger A., Tiwari S., Problem definitions and evaluation criteria for the CEC 2005 special session on real parameter optimization, Kanpur Genetic Algorithms Lab., IIT Kanpur, Nanyang Technol. Univ., Singapore, KanGAL Rep. 2005005 (2005).
• [14] Di Barba G., Savini A., Global optimization of Loney’s solenoid: a benchmark problem, International Journal of Applied Electromagnetics and Mechanics, vol. 6, no. 4, pp. 273–276 (1995).
• [15] Klein C.E., Segundo E.H.V., Mariani V.C., Coelho L.D.S., Modified Social-Spider Optimization Algorithm Applied to Electromagnetic Optimization, IEEE Transactions on Magnetics, vol. 52, no. 3 (2015), DOI: 10.1109/TMAG.2015.2483059.
• [16] Ye X., Wang P., Impact of migration strategies and individual stabilization on multi-scale quantum harmonic oscillator algorithm for global numerical optimization problems, Applied Soft Computing, vol. 85 (2019), DOI: 10.1016/j.asoc.2019.105800.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).