Research Article
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Year 2025, Volume: 13 Issue: 2, 42 - 60, 31.05.2025
https://doi.org/10.21541/apjess.1682052

Abstract

References

  • J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, 5th ed. Cambridge: The MIT Press, 1992. doi: 10.7551/mitpress/1090.001.0001.
  • N. G. Yarushkina, ‘Genetic algorithms for engineering optimization: theory and practice’, in Proceedings of IEEE International Conference on Artificial Intelligence Systems (ICAIS 2002), Divnomorskoe, Russia, Sep. 2002, pp. 357–362. doi: 10.1109/ICAIS.2002.1048127.
  • E. Ileberi, Y. Sun, and Z. Wang, ‘A machine learning based credit card fraud detection using the GA algorithm for feature selection’, J. Big Data, vol. 9, no. 1, pp. 1–17, Feb. 2022, doi: 10.1186/s40537-022-00573-8.
  • C. Buiu and I. Dumitrache, ‘Genetic Algorithms in Intelligent Control Systems Design’, IFAC Proc. Vol., vol. 27, no. 3, pp. 181–186, Jun. 1994, doi: 10.1016/S1474-6670(17)46106-4.
  • B. Mirzaeian, M. Moallem, V. Tahani, and C. Lucas, ‘Multiobjective optimization method based on a genetic algorithm for switched reluctance motor design’, IEEE Trans. Magn., vol. 38, no. 3, pp. 1524–1527, May 2002, doi: 10.1109/20.999126.
  • T.-P. Hong, H.-S. Wang, W.-Y. Lin, and W.-Y. Lee, ‘Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process’, Appl. Intell., vol. 16, no. 1, pp. 7–17, Jan. 2002, doi: 10.1023/A:1012815625611.
  • E. Mezura-Montes and C. A. C. Coello, ‘A simple multimembered evolution strategy to solve constrained optimization problems’, IEEE Trans. Evol. Comput., vol. 9, no. 1, pp. 1–17, Feb. 2005, doi: 10.1109/TEVC.2004.836819.
  • F. Zhang, X. Cao, and D. Yang, ‘Intelligent scheduling of public traffic vehicles based on a hybrid genetic algorithm’, Tsinghua Sci. Technol., vol. 13, no. 5, pp. 625–631, Oct. 2008, doi: 10.1016/S1007-0214(08)70103-2.
  • K. Choi, D.-H. Jang, S.-I. Kang, J.-H. Lee, T.-K. Chung, and H.-S. Kim, ‘Hybrid Algorithm Combing Genetic Algorithm With Evolution Strategy for Antenna Design’, IEEE Trans. Magn., vol. 52, no. 3, pp. 1–4, Mar. 2016, doi: 10.1109/TMAG.2015.2486043.
  • K. B. Ali, A. J. Telmoudi, and S. Gattoufi, ‘Improved Genetic Algorithm Approach Based on New Virtual Crossover Operators for Dynamic Job Shop Scheduling’, IEEE Access, vol. 8, pp. 213318–213329, Dec. 2020, doi: 10.1109/ACCESS.2020.3040345.
  • E. Ergül, ‘Çok amaçlı genetik algoritma yöntemlerinin başarımının belirlenmesi için iki yeni ölçüt önerisi’, İleri Teknol. Bilim. Derg., vol. 4, no. 1, pp. 1–13, Mar. 2015.
  • P. Sharma and S. Raju, ‘Metaheuristic optimization algorithms: a comprehensive overview and classification of benchmark test functions’, Soft Comput., vol. 28, no. 4, pp. 3123–3186, Feb. 2024, doi: 10.1007/s00500-023-09276-5.
  • E. Osaba et al., ‘A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems’, Swarm Evol. Comput., vol. 64, p. 100888, Jul. 2021, doi: 10.1016/j.swevo.2021.100888.
  • K. Yang et al., ‘Unified Selective Harmonic Elimination for Multilevel Converters’, IEEE Trans. Power Electron., vol. 32, no. 2, pp. 1579–1590, Feb. 2017, doi: 10.1109/TPEL.2016.2548080.
  • M. S. A. Dahidah, G. Konstantinou, and V. G. Agelidis, ‘A Review of Multilevel Selective Harmonic Elimination PWM: Formulations, Solving Algorithms, Implementation and Applications’, IEEE Trans. Power Electron., vol. 30, no. 8, pp. 4091–4106, Aug. 2015, doi: 10.1109/TPEL.2014.2355226.
  • S. P. Sunddararaj, S. R. Srinivasarangan, and S. Nallusamy, ‘An Extensive Review of Multilevel Inverters Based on Their Multifaceted Structural Configuration, Triggering Methods and Applications’, Electronics, vol. 9, no. 3, pp. 1–33, Mar. 2020, doi: 10.3390/electronics9030433.
  • P. M. Lingom, J. Song-Manguelle, J. M. Nyobe-Yome, and M. L. Doumbia, ‘A Comprehensive Review of Compensation Control Techniques Suitable for Cascaded H-Bridge Multilevel Inverter Operation with Unequal DC Sources or Faulty Cells’, Energies, vol. 17, no. 3, pp. 1–17, Jan. 2024, doi: 10.3390/en17030722.
  • W. Mao et al., ‘A Research on Cascaded H-Bridge Module Level Photovoltaic Inverter Based on a Switching Modulation Strategy’, Energies, vol. 12, no. 10, pp. 1–17, Jan. 2019, doi: 10.3390/en12101851.
  • T. Bertin, G. Despesse, and R. Thomas, ‘Comparison between a Cascaded H-Bridge and a Conventional H-Bridge for a 5-kW Grid-Tied Solar Inverter’, Electronics, vol. 12, no. 8, pp. 1–25, Jan. 2023, doi: 10.3390/electronics12081929.
  • Y. Bektaş, H. Karaca, T. A. Taha, and H. I. Zaynal, ‘Red deer algorithm-based selective harmonic elimination technique for multilevel inverters’, Bull. Electr. Eng. Inform., vol. 12, no. 5, pp. 2643–2650, Oct. 2023, doi: 10.11591/eei.v12i5.5160.
  • A. Brindle, ‘Genetic algorithms for function optimization’, Ph.D dissertation, The University of Alberta, Edmonton, 1980. doi: 10.7939/R3FB4WS2W.
  • A. K. De Jong, ‘Analysis of the behavior of a class of genetic adaptive systems’, Ph.D dissertation, The University of Michigan, Hampton, 1975. [Online]. Available: http://deepblue.lib.umich.edu/handle/2027.42/4507
  • A. Agapie and A. H. Wright, ‘Theoretical analysis of steady state genetic algorithms’, Appl. Math., vol. 59, no. 5, pp. 509–525, Oct. 2014, doi: 10.1007/s10492-014-0069-z.
  • A. Hassanat, K. Almohammadi, E. Alkafaween, E. Abunawas, A. Hammouri, and V. B. S. Prasath, ‘Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach’, Information, vol. 10, no. 12, pp. 1–36, Dec. 2019, doi: 10.3390/info10120390.
  • K. Duan, S. Fong, S. W. I. Siu, W. Song, and S. S.-U. Guan, ‘Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments’, Symmetry, vol. 10, no. 5, pp. 1–13, May 2018, doi: 10.3390/sym10050168.
  • W.-Y. Lin, W.-Y. Lee, and T.-P. Hong, ‘Adapting Crossover and Mutation Rates in Genetic Algorithms’, J Inf Sci Eng, vol. 19, pp. 889–903, 2003.
  • M. Dong and Y. Wu, ‘Dynamic Crossover and Mutation Genetic Algorithm Based on Expansion Sampling’, in Proceedings of International Conference on Artificial Intelligence and Computational Intelligence (AICI 2009), Shanghai, China, Nov. 2009, pp. 141–149. doi: 10.1007/978-3-642-05253-8_16.

Comparative Analysis of GA Variants for Solving the SHE Problem in Multilevel Inverters

Year 2025, Volume: 13 Issue: 2, 42 - 60, 31.05.2025
https://doi.org/10.21541/apjess.1682052

Abstract

In recent years, the Cascaded H-Bridge Multilevel Inverter (CHB-MLI) topology, controlled by the Selective Harmonic Elimination (SHE) method, has been widely preferred in high-power applications —especially where power quality is critical— due to its superior output quality, low switching losses, and reduced voltage stress on the power switches. However, since the SHE method involves nonlinear transcendental equations, it becomes evident that analytical solution methods are insufficient as the inverter voltage levels increase. To overcome this challenge, researchers frequently resort to metaheuristic optimization algorithms. In this context, the present study compares the performance of different Genetic Algorithm (GA) variants in solving the SHE equations of a Cascaded H-Bridge Multilevel Inverter (CHB-MLI) system. A total of eight algorithms, including the Standard GA (SGA) and several improved variants from the literature, were evaluated. Each algorithm was tested through independent runs to determine the optimal switching angles which minimize a fitness function specifically created for the SHE equations. The obtained optimal switching angles were applied to a CHB-MLI system modelled in the MATLAB/Simulink environment, and the performance of each variant was analysed based on output voltage quality, fundamental component accuracy, and Total Harmonic Distortion (THD) criteria. The results reveal the strengths and weaknesses of different variants in the optimization process and indicate the potential of a well-designed GA as an effective alternative for addressing complex engineering problems, such as the solution of SHE equations.

References

  • J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, 5th ed. Cambridge: The MIT Press, 1992. doi: 10.7551/mitpress/1090.001.0001.
  • N. G. Yarushkina, ‘Genetic algorithms for engineering optimization: theory and practice’, in Proceedings of IEEE International Conference on Artificial Intelligence Systems (ICAIS 2002), Divnomorskoe, Russia, Sep. 2002, pp. 357–362. doi: 10.1109/ICAIS.2002.1048127.
  • E. Ileberi, Y. Sun, and Z. Wang, ‘A machine learning based credit card fraud detection using the GA algorithm for feature selection’, J. Big Data, vol. 9, no. 1, pp. 1–17, Feb. 2022, doi: 10.1186/s40537-022-00573-8.
  • C. Buiu and I. Dumitrache, ‘Genetic Algorithms in Intelligent Control Systems Design’, IFAC Proc. Vol., vol. 27, no. 3, pp. 181–186, Jun. 1994, doi: 10.1016/S1474-6670(17)46106-4.
  • B. Mirzaeian, M. Moallem, V. Tahani, and C. Lucas, ‘Multiobjective optimization method based on a genetic algorithm for switched reluctance motor design’, IEEE Trans. Magn., vol. 38, no. 3, pp. 1524–1527, May 2002, doi: 10.1109/20.999126.
  • T.-P. Hong, H.-S. Wang, W.-Y. Lin, and W.-Y. Lee, ‘Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process’, Appl. Intell., vol. 16, no. 1, pp. 7–17, Jan. 2002, doi: 10.1023/A:1012815625611.
  • E. Mezura-Montes and C. A. C. Coello, ‘A simple multimembered evolution strategy to solve constrained optimization problems’, IEEE Trans. Evol. Comput., vol. 9, no. 1, pp. 1–17, Feb. 2005, doi: 10.1109/TEVC.2004.836819.
  • F. Zhang, X. Cao, and D. Yang, ‘Intelligent scheduling of public traffic vehicles based on a hybrid genetic algorithm’, Tsinghua Sci. Technol., vol. 13, no. 5, pp. 625–631, Oct. 2008, doi: 10.1016/S1007-0214(08)70103-2.
  • K. Choi, D.-H. Jang, S.-I. Kang, J.-H. Lee, T.-K. Chung, and H.-S. Kim, ‘Hybrid Algorithm Combing Genetic Algorithm With Evolution Strategy for Antenna Design’, IEEE Trans. Magn., vol. 52, no. 3, pp. 1–4, Mar. 2016, doi: 10.1109/TMAG.2015.2486043.
  • K. B. Ali, A. J. Telmoudi, and S. Gattoufi, ‘Improved Genetic Algorithm Approach Based on New Virtual Crossover Operators for Dynamic Job Shop Scheduling’, IEEE Access, vol. 8, pp. 213318–213329, Dec. 2020, doi: 10.1109/ACCESS.2020.3040345.
  • E. Ergül, ‘Çok amaçlı genetik algoritma yöntemlerinin başarımının belirlenmesi için iki yeni ölçüt önerisi’, İleri Teknol. Bilim. Derg., vol. 4, no. 1, pp. 1–13, Mar. 2015.
  • P. Sharma and S. Raju, ‘Metaheuristic optimization algorithms: a comprehensive overview and classification of benchmark test functions’, Soft Comput., vol. 28, no. 4, pp. 3123–3186, Feb. 2024, doi: 10.1007/s00500-023-09276-5.
  • E. Osaba et al., ‘A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems’, Swarm Evol. Comput., vol. 64, p. 100888, Jul. 2021, doi: 10.1016/j.swevo.2021.100888.
  • K. Yang et al., ‘Unified Selective Harmonic Elimination for Multilevel Converters’, IEEE Trans. Power Electron., vol. 32, no. 2, pp. 1579–1590, Feb. 2017, doi: 10.1109/TPEL.2016.2548080.
  • M. S. A. Dahidah, G. Konstantinou, and V. G. Agelidis, ‘A Review of Multilevel Selective Harmonic Elimination PWM: Formulations, Solving Algorithms, Implementation and Applications’, IEEE Trans. Power Electron., vol. 30, no. 8, pp. 4091–4106, Aug. 2015, doi: 10.1109/TPEL.2014.2355226.
  • S. P. Sunddararaj, S. R. Srinivasarangan, and S. Nallusamy, ‘An Extensive Review of Multilevel Inverters Based on Their Multifaceted Structural Configuration, Triggering Methods and Applications’, Electronics, vol. 9, no. 3, pp. 1–33, Mar. 2020, doi: 10.3390/electronics9030433.
  • P. M. Lingom, J. Song-Manguelle, J. M. Nyobe-Yome, and M. L. Doumbia, ‘A Comprehensive Review of Compensation Control Techniques Suitable for Cascaded H-Bridge Multilevel Inverter Operation with Unequal DC Sources or Faulty Cells’, Energies, vol. 17, no. 3, pp. 1–17, Jan. 2024, doi: 10.3390/en17030722.
  • W. Mao et al., ‘A Research on Cascaded H-Bridge Module Level Photovoltaic Inverter Based on a Switching Modulation Strategy’, Energies, vol. 12, no. 10, pp. 1–17, Jan. 2019, doi: 10.3390/en12101851.
  • T. Bertin, G. Despesse, and R. Thomas, ‘Comparison between a Cascaded H-Bridge and a Conventional H-Bridge for a 5-kW Grid-Tied Solar Inverter’, Electronics, vol. 12, no. 8, pp. 1–25, Jan. 2023, doi: 10.3390/electronics12081929.
  • Y. Bektaş, H. Karaca, T. A. Taha, and H. I. Zaynal, ‘Red deer algorithm-based selective harmonic elimination technique for multilevel inverters’, Bull. Electr. Eng. Inform., vol. 12, no. 5, pp. 2643–2650, Oct. 2023, doi: 10.11591/eei.v12i5.5160.
  • A. Brindle, ‘Genetic algorithms for function optimization’, Ph.D dissertation, The University of Alberta, Edmonton, 1980. doi: 10.7939/R3FB4WS2W.
  • A. K. De Jong, ‘Analysis of the behavior of a class of genetic adaptive systems’, Ph.D dissertation, The University of Michigan, Hampton, 1975. [Online]. Available: http://deepblue.lib.umich.edu/handle/2027.42/4507
  • A. Agapie and A. H. Wright, ‘Theoretical analysis of steady state genetic algorithms’, Appl. Math., vol. 59, no. 5, pp. 509–525, Oct. 2014, doi: 10.1007/s10492-014-0069-z.
  • A. Hassanat, K. Almohammadi, E. Alkafaween, E. Abunawas, A. Hammouri, and V. B. S. Prasath, ‘Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach’, Information, vol. 10, no. 12, pp. 1–36, Dec. 2019, doi: 10.3390/info10120390.
  • K. Duan, S. Fong, S. W. I. Siu, W. Song, and S. S.-U. Guan, ‘Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments’, Symmetry, vol. 10, no. 5, pp. 1–13, May 2018, doi: 10.3390/sym10050168.
  • W.-Y. Lin, W.-Y. Lee, and T.-P. Hong, ‘Adapting Crossover and Mutation Rates in Genetic Algorithms’, J Inf Sci Eng, vol. 19, pp. 889–903, 2003.
  • M. Dong and Y. Wu, ‘Dynamic Crossover and Mutation Genetic Algorithm Based on Expansion Sampling’, in Proceedings of International Conference on Artificial Intelligence and Computational Intelligence (AICI 2009), Shanghai, China, Nov. 2009, pp. 141–149. doi: 10.1007/978-3-642-05253-8_16.
There are 27 citations in total.

Details

Primary Language English
Subjects Theory of Computation (Other)
Journal Section Research Article
Authors

Hüseyin Doğan 0000-0002-9609-7825

Submission Date April 22, 2025
Acceptance Date May 8, 2025
Early Pub Date May 30, 2025
Publication Date May 31, 2025
Published in Issue Year 2025 Volume: 13 Issue: 2

Cite

IEEE H. Doğan, “Comparative Analysis of GA Variants for Solving the SHE Problem in Multilevel Inverters”, APJESS, vol. 13, no. 2, pp. 42–60, 2025, doi: 10.21541/apjess.1682052.

Academic Platform Journal of Engineering and Smart Systems