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An improved gravitational search algorithm for solving an electromagnetic design problem

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Abstract

The gravitational search algorithm (GSA) is a novel optimization technique that relies upon the law of motion and law of gravity of masses to describe the interaction between the agents. The GSA has shown outstanding performance but suffers from the drawback of a slow process due to the dependence of the fitness function on the masses of the agents. As a result, after each iteration, the masses get heavier, restricting their movement. Due to this effect, the masses cancel out the gravitational forces on each other, preventing them from finding the optimum quickly. To overcome this limitation, an improved GSA based on a modified exploitation strategy is proposed herein. The primary aim of this modification is to enhance the performance of the algorithm in terms of faster convergence and avoidance of premature convergence. An electromagnetic optimization problem is used to validate the performance of the presented method. The simulation results confirm that the proposed method provides outstanding results in solving Loney’s solenoid design problem and that the stability of the solution is much better compared with those obtained using the standard gravitational search algorithm or various other state-of-the-art techniques that have previously been applied to solve this problem.

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References

  1. dos Santos Coelho, L., Alotto, P.: Gaussian artificial bee colony algorithm approach applied to Loney’s solenoid benchmark problem. In: Digests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation, pp. 1–1 (2010)

  2. Cogotti, E., Fanni, A., Pilo, F.: Comparison of optimization techniques for Loney’s solenoids design: an alternative Tabu Search algorithm. IEEE Trans. Magn. 36, 1153–1157 (2000)

    Article  Google Scholar 

  3. Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)

    Article  Google Scholar 

  4. Ji, B., Yuan, X., Li, X., Huang, Y., Li, W.: Application of quantum-inspired binary gravitational search algorithm for thermal unit commitment with wind power integration. Energy Convers. Manag. 87, 589–598 (2014)

    Article  Google Scholar 

  5. Jahan, M.S., Amjady, N.: Solution of large-scale security constrained optimal power flow by a new bi-level optimisation approach based on enhanced gravitational search algorithm. IET Gener. Transm. Distrib. 7(12), 1481–1491 (2013)

    Article  Google Scholar 

  6. Radosavljević, J., Jevtić, M., Arsić, N., Klimenta, D.: Optimal power flow for distribution networks using gravitational search algorithm. Electr. Eng. 96(4), 335–345 (2014)

    Article  Google Scholar 

  7. Jiang, S., Ji, Z., Shen, Y.: A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints. Int. J. Electr. Power Energy Syst. 55, 628–644 (2014)

    Article  Google Scholar 

  8. Rashedi, E., Zarezadeh, A.: Noise filtering in ultrasound images using gravitational search algorithm. In: 2014 Iranian Conference on Intelligent Systems (ICIS), pp. 1–4 (2014)

  9. Gupta, C., Jain, S.: Multilevel fuzzy partition segmentation of satellite images using GSA. In: 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT 2014), pp. 173–178

  10. Zhao, W.: Adaptive image enhancement based on gravitational search algorithm. Procedia Eng. 15, 3288–3292 (2011)

    Article  Google Scholar 

  11. Belabad, A.R., Sharifian, S., Motamedi, S.A., Gholizadeh, N.: A novel model for digital predistortion based on a gravitational search algorithm for linearization of transmitters in LTE networks. J. Comput. Electron. (2019). https://www.springerprofessional.de/en/a-novel-model-for-digital-predistortion-based-on-a-gravitational/17328852

  12. Swain, P., Mohanty, S.K., Mangaraj, B.B.: Linear dipole antenna array design and optimization using gravitational search algorithm. In: 2016 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), pp. 514–518 (2016)

  13. Kumari, P.A., Prabha, I.S.: Optimum network selection in heterogeneous wireless environment using gravitational search algorithm. In: 2015 International Conference on Signal Processing and Communication Engineering Systems, pp. 464–467 (2015)

  14. Wei, L., Ma, B.: Application of improved gravitational search algorithm in PID control for boiler drum water level. In: 2017 29th Chinese Control and Decision Conference (CCDC), pp. 1852–1857 (2017)

  15. Aziz, M.S.I.B., Nawawi, S.W., Sudin, S., Wahab, N.A.: Exploitation selection of alpha parameter in gravitational search algorithm of PID controller for computational time analysis. In: 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), pp. 112–117 (2014)

  16. Duman, S., Maden, D., Güvenç, U.: Determination of the PID controller parameters for speed and position control of DC motor using gravitational search algorithm. In: 2011 7th International Conference on Electrical and Electronics Engineering (ELECO), pp. I-225–I-229 (2011)

  17. Xiao, J., Cheng, Z.: DNA sequences optimization based on gravitational search algorithm for reliable DNA computing. In: 2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications, pp. 103–107 (2011)

  18. Zemali, E., Boukra, A.: EGSA: a new enhanced gravitational search algorithm to resolve multiple sequence alignment problem. Int. J. Intell. Eng. Inform. 6, 204 (2018)

    Google Scholar 

  19. Amoozegar, M., Nezamabadi-pour, H.: Software performance optimization based on constrained GSA. In: The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), pp. 134–139 (2012)

  20. Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: BGSA: binary gravitational search algorithm. Nat. Comput. 9(3), 727–745 (2010)

    Article  MathSciNet  Google Scholar 

  21. Rashedi, E., Rashedi, E., Nezamabadi-pour, H.: A comprehensive survey on gravitational search algorithm. Swarm Evolut. Comput. 41, 141–158 (2018)

    Article  Google Scholar 

  22. He, S., Zhu, L., Wang, L., Yu, L., Yao, C.: A modified gravitational search algorithm for function optimization. IEEE Access 7, 5984–5993 (2019)

    Article  Google Scholar 

  23. Di Barba, G., Savini, A.: Global optimization of Loney’s solenoid: a benchmark problem. Int. J. Appl. Electromagn. Mech. 6(4), 273–276 (1995)

    Google Scholar 

  24. Andrei, M.-I., Caciulan, E., Dan, D., Ciuprina, G., Ioan, D.: Matlab based parallel deterministic optimization of the Loney’s solenoid (2010). https://www.researchgate.net/publication/235998150_Matlab_Based_Parallel_Deterministic_Optimization_of_the_Loney's_Solenoid

  25. dos Santos Coelho, L., Alotto, P.: Gaussian artificial bee colony algorithm approach applied to Loney’s Solenoid benchmark problem. IEEE Trans. Magn. 47(5), 1326–1329 (2011)

    Article  Google Scholar 

  26. Taherdangkoo, M.: Modified BNMR algorithm applied to Loney’s solenoid benchmark problem. Int. J. Appl. Electromagn. Mech. 46, 683 (2014)

    Article  Google Scholar 

  27. Coelho, L.D.S., Alotto, P.: Tribes optimization algorithm applied to the Loney’s solenoid. IEEE Trans. Magn. 45(3), 1526–1529 (2009)

    Article  Google Scholar 

  28. Duca, A., Duca, L.-C., Ciuprina, G., Ioan, D.: Neighborhood strategies for QPSO algorithms to solve benchmark electromagnetic problems, pp. 148–155 (2016)

  29. Ciuprina, G., Ioan, D., Munteanu, I.: Use of intelligent-particle swarm optimization in electromagnetics. IEEE Trans. Magn. 38(2), 1037–1040 (2002)

    Article  Google Scholar 

  30. Taherdangkoo, M.: Modified stem cells algorithm for Loney’s solenoid benchmark problem. Int. J. Appl. Electromagn. Mech. 42, 437 (2013)

    Article  Google Scholar 

  31. Duca, A., Ciuprina, G., Lup, S., Hameed, I.: ACO R algorithm’s efficiency for electromagnetic optimization benchmark problems, pp. 1–5 (2019)

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Authors and Affiliations

  1. University of Technology Sydney, Ultimo, NSW, 2007, Australia

    Talha Ali Khan & Sai Ho Ling

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  1. Talha Ali Khan
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  2. Sai Ho Ling
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Correspondence to Talha Ali Khan.

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Khan, T.A., Ling, S.H. An improved gravitational search algorithm for solving an electromagnetic design problem. J Comput Electron 19, 773–779 (2020). https://doi.org/10.1007/s10825-020-01476-8

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  • DOI: https://doi.org/10.1007/s10825-020-01476-8

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