Cover
Vol. 20 No. 2 (2024)

Published: December 31, 2024

Pages: 190-206

Original Article

A New Algorithm Based on Pitting Corrosion for Engineering Design Optimization Problems

Abstract

This paper presents a new optimization algorithm called corrosion diffusion optimization algorithm (CDOA). The proposed algorithm is based on the diffusion behavior of the pitting corrosion on the metal surface. CDOA utilizes the oxidation and reduction electrochemical reductions as well as the mathematical model of Gibbs free energy in its searching for the optimal solution of a certain problem. Unlike other algorithms, CDOA has the advantage of dispensing any parameter that need to be set for improving the convergence toward the optimal solution. The superiority of the proposed algorithm over the others is highlighted by applying them on some unimodal and multimodal benchmark functions. The results show that CDOA has better performance than the other algorithms in solving the unimodal equations regardless the dimension of the variable. On the other hand, CDOA provides the best multimodal optimization solution for dimensions less than or equal to (5, 10, 15, up to 20) but it fails in solving this type of equations for variable dimensions larger than 20. Moreover, the algorithm is also applied on two engineering application problems, namely the PID controller and the cantilever beam to accentuate its high performance in solving the engineering problems. The proposed algorithm results in minimized values for the settling time, rise time, and overshoot for the PID controller. Where the rise time, settling time, and maximum overshoot are reduced in the second order system to 0.0099, 0.0175 and 0.005 sec., in the fourth order system to 0.0129, 0.0129 and 0 sec, in the fifth order system to 0.2339, 0.7756 and 0, in the fourth system which contains time delays to 1.5683, 2.7102 and 1.80 E-4 sec., and in the simple mass-damper system to 0.403, 0.628 and 0 sec., respectively. In addition, it provides the best fitness function for the cantilever beam problem compared with some other well-known algorithms.

References

  1. I. Fister Jr, X.-S. Yang, I. Fister, J. Brest, and D. Fis- ter, “A brief review of nature-inspired algorithms for optimization,” arXiv preprint arXiv:1307.4186, 2013.
  2. C. Blum, “Ant colony optimization: Introduction and recent trends,” Physics of Life reviews, vol. 2, no. 4, pp. 353–373, 2005.
  3. X.-S. Yang, “Firefly algorithm, stochastic test functions and design optimisation,” International journal of bio- inspired computation, vol. 2, no. 2, pp. 78–84, 2010.
  4. J. Kennedy and R. Eberhart, “Particle swarm optimiza- tion,” in Proceedings of ICNN’95-international confer- ence on neural networks, vol. 4, pp. 1942–1948, ieee, 1995.
  5. X.-S. Yang and S. Deb, “Engineering optimisation by cuckoo search,” International Journal of Mathematical Modelling and Numerical Optimisation, vol. 1, no. 4, pp. 330–343, 2010.
  6. C. Zhao and Y. Zhou, “A complex encoding flower polli- nation algorithm for global numerical optimization,” in Intelligent Computing Theories and Application: 12th International Conference, ICIC 2016, Lanzhou, China, August 2-5, 2016, Proceedings, Part I 12, pp. 667–678, Springer, 2016.
  7. M. Azizi, S. Talatahari, N. Khodadadi, and P. Sareh, “Multiobjective atomic orbital search (moaos) for global and engineering design optimization,” IEEE Access, vol. 10, pp. 67727–67746, 2022.
  8. Z. Zandi, E. Afjei, and M. Sedighizadeh, “Reactive power dispatch using big bang-big crunch optimization algorithm for voltage stability enhancement,” in 2012 IEEE International Conference on Power and Energy (PECon), pp. 239–244, IEEE, 2012.
  9. R. Formato, “Central force optimization: a new meta- heuristic with applications in applied electromagnetics,” Progress in electromagnetics research, vol. 77, pp. 425– 491, 2007.
  10. G. Zhao, X. Wang, H. Zhao, and Z. Jiang, “An im- proved pedestrian dead reckoning algorithm based on smartphone built-in mems sensors,” AEU-International Journal of Electronics and Communications, vol. 168, p. 154674, 2023.
  11. R. S. Ali, F. M. Alnahwi, and A. S. Abdullah, “A modi- fied camel travelling behaviour algorithm for engineer- ing applications,” Australian Journal of Electrical and Electronics Engineering, vol. 16, no. 3, pp. 176–186, 2019.
  12. T.-C. Chen, P.-W. Tsai, S.-C. Chu, and J.-S. Pan, “A novel optimization approach: bacterial-ga foraging,” in 205 | Al-mtory, Alnahwi & Ali Second international conference on innovative comput- ing, Informatio and Control (ICICIC 2007), pp. 391– 391, IEEE, 2007.
  13. X.-S. Yang, “A new metaheuristic bat-inspired algo- rithm,” in Nature inspired cooperative strategies for optimization (NICSO 2010), pp. 65–74, Springer, 2010.
  14. D. Teodorovic and M. Dell’Orco, “Bee colony optimization–a cooperative learning approach to com- plex transportation problems,” Advanced OR and AI methods in transportation, vol. 51, p. 60, 2005.
  15. X.-S. Yang, J. M. Lees, and C. T. Morley, “Application of virtual ant algorithms in the optimization of cfrp shear strengthened precracked structures,” in International Conference on Computational Science, pp. 834–837, Springer, 2006.
  16. F. M. Alnahwi, Y. I. Al-Yasir, D. Sattar, R. S. Ali, C. H. See, and R. A. Abd-Alhameed, “A new optimization al- gorithm based on the fungi kingdom expansion behavior for antenna applications,” Electronics, vol. 10, no. 17, p. 2057, 2021.
  17. D. Simon, “Biogeography-based optimization,” IEEE transactions on evolutionary computation, vol. 12, no. 6, pp. 702–713, 2008.
  18. Y. Shi, “An optimization algorithm based on brainstorm- ing process,” in Emerging Research on Swarm Intelli- gence and Algorithm Optimization, pp. 1–35, IGI Global, 2015.
  19. C. J. Bastos Filho, F. B. de Lima Neto, A. J. Lins, A. I. Nascimento, and M. P. Lima, “A novel search algorithm based on fish school behavior,” in 2008 IEEE inter- national conference on systems, man and cybernetics, pp. 2646–2651, IEEE, 2008.
  20. M. M. Eusuff and K. E. Lansey, “Optimization of water distribution network design using the shuffled frog leap- ing algorithm,” Journal of Water Resources planning and management, vol. 129, no. 3, pp. 210–225, 2003.
  21. A. Hatamlou, “Black hole: A new heuristic optimiza- tion approach for data clustering,” Information sciences, vol. 222, pp. 175–184, 2013.
  22. A. Kaveh and S. Talatahari, “A novel heuristic optimiza- tion method: charged system search,” Acta mechanica, vol. 213, no. 3, pp. 267–289, 2010.
  23. H. Eskandar, A. Sadollah, A. Bahreininejad, and M. Hamdi, “Water cycle algorithm–a novel metaheuris- tic optimization method for solving constrained engi- neering optimization problems,” Computers & Struc- tures, vol. 110, pp. 151–166, 2012.
  24. H. Shayeghi and J. Dadashpour, “Anarchic society op- timization based pid control of an automatic voltage regulator (avr) system,” Electrical and electronic engi- neering, vol. 2, no. 4, pp. 199–207, 2012.
  25. P. Civicioglu, “Artificial cooperative search algorithm for numerical optimization problems,” Information Sci- ences, vol. 229, pp. 58–76, 2013.
  26. D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE transactions on evolu- tionary computation, vol. 1, no. 1, pp. 67–82, 1997.
  27. C. M. Hansson, “The impact of corrosion on society,” Metallurgical and Materials Transactions A, vol. 42, pp. 2952–2962, 2011.
  28. Z. Szklarska-Smialowska and ZS-Smialowska, Pitting and crevice corrosion, vol. 446. NACE international Houston, TX, 2005.
  29. C. Andrade and C. Alonso, “Corrosion rate monitoring in the laboratory and on-site,” Construction and building materials, vol. 10, no. 5, pp. 315–328, 1996.
  30. M. I. I. Bin, “Computational model of pitting corrosion,” 2013.
  31. Z. Ahmad, Principles of corrosion engineering and cor- rosion control. Elsevier, 2006.
  32. N. Perez, Electrochemistry and corrosion science. Springer, 2004.
  33. E. E. Stansbury and R. A. Buchanan, Fundamentals of electrochemical corrosion. ASM international, 2000.
  34. M. G. Fontana, N. D. Greene, et al., Corrosion engineer- ing. McGraw-hill, 2018.
  35. G. Frankel, “Pitting corrosion,” 2003.
  36. H. Kaesche, Corrosion of metals: physicochemical prin- ciples and current problems. Springer Science & Busi- ness Media, 2012.
  37. M. Molga and C. Smutnicki, “Test functions for opti- mization needs,” Test functions for optimization needs, vol. 101, p. 48, 2005. 206 | Al-mtory, Alnahwi & Ali
  38. A. G. H. et al, “Tcrow search algorithm: theory, re- cent advances, and applications,” IEEE Access, vol. 8, pp. 173548–173565, 2020.
  39. A. I. Hafez, H. M. Zawbaa, E. Emary, and A. E. Has- sanien, “Sine cosine optimization algorithm for feature selection,” in 2016 international symposium on innova- tions in intelligent systems and applications (INISTA), pp. 1–5, IEEE, 2016.
  40. K. H. Raut and S. Vaishnav, “Performance analysis of pid tuning techniques based on time response specifi- cation,” International Journal of Innovative Research in Electrical, Electronics, Instrumentation & Control Engineering, vol. 2, no. 1, 2014.
  41. I. Juniku and P. Marango, “Pid design with bio-inspired intelligent algorithms for high order systems,” Interna- tional Journal of Mathematics and Computers in Simu- lation, vol. 9, pp. 44–52, 2015.
  42. L. Shirokov, P. Chelyshkov, and E. Romanenko, “Au- tomated management of engineering infrastructure of pools of different function,” in MATEC Web of Confer- ences, vol. 86, p. 04062, EDP Sciences, 2016.
  43. I. Juniku and P. Marango, “A comparison of pso and bfo applications for the pid controller synthesis in time-delay systems,”
  44. S. Mirjalili, S. M. Mirjalili, and A. Hatamlou, “Multi- verse optimizer: a nature-inspired algorithm for global optimization,” Neural Computing and Applications, vol. 27, pp. 495–513, 2016.
  45. https://www3.ntu.edu.sg/home/epnsugan/,” 2015.