International Journal of Wireless and Microwave Technologies (IJWMT)
IJWMT Vol. 16, No. 2, 8 Apr. 2026
Cover page and Table of Contents: PDF (size: 2252KB)
Computational Intelligence-Based Evaluation of Propagation Modes in Planar Optical Waveguide using PSO and GA
PDF (2252KB), PP.118-138
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Author(s)
Jannatul Ferdoush Jannati
1,*
Sayeda Parvin
1
Md. Imdadul Islam
1
Index Terms
Optical fiber mode, incident angle, graphical solution, PSO, GA, Computational Intelligence
Abstract
The solution of the modal equation of a planar optical waveguide is a cumbersome job and usually incident angle of successful modes is determined by a graphical solution. In this research work, we applied two computational intelligence methods: Particle Swarm Optimization (PSO) and Genetic algorithm (GA) in a segment-wise approach to solving the modal equation of the tangent function. The motivation for employing Computational Intelligence (CI) lies in its ability to optimize functions without requiring high-level mathematics or complex statistical models, as opposed to traditional analytical methods. This strategic use of computational intelligence significantly reduces the overall computational cost, more nature inspired and probabilistic, providing an efficient alternative. Particularly for functions with complex solutions, the utilization of computational intelligence or soft computing methods becomes imperative to obtain an approximate solution compared to classical numerical optimization methods like Newton-Raphson, bisection etc. that generally deterministic and aim to find the exact optimal solution. In terms of using probability (a core component of chosen algorithm’s searching mechanism) we can incorporate distributions that will enhance the performance. Therefore, while classical root-finding methods are computationally simpler for isolated cases, the use of PSO and GA is motivated by their global search capability, robustness to initialization, and ease of automation, which are advantageous in generalized or large-scale modal solution frameworks. The outcomes derived from both methods (PSO and GA) are meticulously compared with the results obtained through the traditional graphical solution. We have found accuracy of 99.95% for PSO and 99.87% for GA. Notably, the findings reveal a close correlation between the computational intelligence approaches and the graphical method offering a promising avenue for advancing the field with a more computationally feasible approach.
Cite This Paper
Jannatul Ferdoush, JannatiSayeda Parvin, Md. Imdadul Islam, "Computational Intelligence-Based Evaluation of Propagation Modes in Planar Optical Waveguide using PSO and GA", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.16, No.2, pp. 118-138, 2026. DOI:10.5815/ijwmt.2026.02.09
Reference
[1]M. R. Sayeh and M. T. Mostafavi, “A novel derivation for number of modes in step-index fiber,” in Proceedings of the International Symposium on High Capacity Optical Networks and Enabling Technologies, Dubai, United Arab Emirates, November 2007, pp. 1–3.
[2]G. Keiser, Optical Fiber Communications, 3rd ed. McGraw-Hill, 2000.
[3]R. Salih, “Design of step-index multimode optical fiber,” in Ibn Al-Haitham International Conference for Pure and Applied Sciences (IHICPS), vol. 1879, 2020, pp. 1–9.
[4]M. Hammadi and H. A. Hussein, “Comparative investigation of graded index optical fiber characteristics by using different materials,” International Journal of Soft Computing and Engineering (IJSCE), vol. 4, no. 1, pp. 88–94, 2014.
[5]Y. Tamura, H. Sakuma, K. Morita, M. Suzuki, Y. Yamamoto, K. Shimada, and K. Hasegawa, “The first 0.14-db/km loss optical fiber and its impact on submarine transmission,” Journal of Lightwave Technology, pp. 44–49, 2018.
[6]M. Nishimura, “Optical fibers and fiber dispersion compensators for high-speed optical communication,” Journal of Optical and Fiber Communications Reports, vol. 2, no. 2, pp. 115–139, 2005.
[7]J. F. Tuttle, R. Vesel, S. Alagarsamy, L. D. Blackburn, and K. Powell, “Sustainable nox emission reduction at a coalfired power station through the use of online neural network modeling and particle swarm optimization,” Control Engineering Practice, vol. 93, 2019.
[8]F. R. Cabezas Soldevilla and F. A. Cabezas Huerta, “Minimization of losses in power systems by reactive power dispatch using particle swarm optimization,” in Proceedings of the 54th International Universities Power Engineering Conference (UPEC), Bucharest, Romania, September 2019, pp. 1–5.
[9]M. Abdel-Basset, L. Abdel-Fatah, and A. K. Sangaiah, “Metaheuristic algorithms: A comprehensive review,” in Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, 2018, pp. 185– 231.
[10]P. Lu, N. Lalam, M. Badar, B. Liu, B. Chorpening, M. Buric, and P. Ohodnicki, “Distributed optical fiber sensing: review and perspective,” Applied Physics Reviews, vol. 6, no. 4, 2019, article 041302.
[11]P. K. Dubey and V. Shukla, “Dispersion in optical fiber communication,” International Journal of Science and Research (IJSR), pp. 236–239, 2014.
[12]E. Parasuraman, “Soliton solutions of kundu-eckhaus equation in birefringent optical fiber with inter-modal dispersion,” Optik, vol. 223, p. 165388, 2020.
[13]M. Eid and A. Rashed, “Fixed scattering section length with variable scattering section dispersion based optical fibers for polarization mode dispersion penalties,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, pp. 1540–1547, 2021.
[14]T. Sasai et al., “Digital longitudinal monitoring of optical fiber communication link,” Journal of Lightwave Technology, vol. 40, no. 8, pp. 2390–2408, 2022.
[15]F. S. Delgado et al., “Propagation characteristics of optical long period fiber gratings using graphical solution methods,” Journal of Microwaves, Optoelectronics and Electromagnetic Applications, vol. 15, no. 2, pp. 135–145, 2016.
[16]G. Gad, “Particle swarm optimization algorithm and its applications: a systematic review,” Archives of Computational Methods in Engineering, vol. 29, no. 5, pp. 2531–2561, 2022.
[17]L. Duan et al., “Improved particle swarm optimization algorithm for enhanced coupling of coaxial optical communication laser,” Optical Fiber Technology, vol. 64, p. 102559, 2021.
[18]M. E. C. Bento, “A hybrid particle swarm optimization algorithm for the wide-area damping control design,” IEEE Transactions on Industrial Informatics, vol. 18, no. 1, pp. 592–599, 2021.
[19]M. Masood, M. M. Fouad, R. Kamal, I. Glesk, and I. U. Khan, “An improved particle swarm algorithm for multiobjectives based optimization in mpls/gmpls networks,” IEEE Access, vol. 7, pp. 137147–137162, 2019.
[20]Mei, P. Liu, C. Lu, J. Wei, and K. Zhang, “Hierarchical optimal scheduling of regional integrated energy power system based on multi-objective particle swarm optimization algorithm,” in Proceedings of the 14th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Changsha, China, January 2022, pp. 110–115.
[21]G. B. Rajendran et al., “Land-use and land-cover classification using a human group-based particle swarm optimization algorithm with an lstm classifier on hybrid pre-processing remote-sensing images,” Remote Sensing, vol. 12, no. 24, p. 4135, 2020.
[22]R. Kashani et al., “Particle swarm optimization variants for solving geotechnical problems: review and comparative analysis,” Archives of Computational Methods in Engineering, vol. 28, pp. 1871–1927, 2021.
[23]K. Singh, S. K. Arya, and V. Kumar, “Performance enhancement of duobinary modulation-based dwdm system using particle swarm optimization,” Journal of Optical Communications, 2023.
[24]M. Atzemourt, Z. Hachkar, Y. Chihab, and A. Farchi, “Beamforming optimization by binary genetic algorithm,” in Proceedings of the 8th International Conference on Optimization and Applications (ICOA), Genoa, Italy, October 2022, pp. 1–5.
[25]Baicoianu, A. Garofide, R.-I. Luca, and M. Vl˘ ad˘ arean, “Structural optimization using genetic algorithms,” in˘ Proceedings of the International Conference on INnovations in Intelligent SysTems and Applications (INISTA), Biarritz, France, August 2022, pp. 1–5.
[26]H. Sharief and M. S. Sairam, “Performance analysis of mimo-rdwt-ofdm system with optimal genetic algorithm,” AEU - International Journal of Electronics and Communications, vol. 111, p. 152912, 2019.
[27]S.-M. Je and J.-H. Huh, “Estimation of future power consumption level in smart grid: Application of fuzzy logic and genetic algorithm on big data platform,” International Journal of Communication Systems, vol. 34, no. 2, p. e4056, 2021.
[28]S. E. Hayber and S. Keser, “3d sound source localization with fiber optic sensor array based on genetic algorithm,” Optical Fiber Technology, vol. 57, p. 102229, 2020.
[29]R. Denis and P. Madhubala, “Hybrid data encryption model integrating multi-objective adaptive genetic algorithm for secure medical data communication over cloud-based healthcare systems,” Multimedia Tools and Applications, vol. 80, pp. 21165–21202, 2021.
[30]E. Li et al., “Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill,” Engineering with Computers, vol. 37, pp. 3519–3540, 2021.
[31]E. Klavir, Mode Theory for Step Index Multi-Mode Fibers, 2022, project work, Ryerson University, Electrical and Computer Engineering.
[32]Y. Dote and S. J. Ovaska, “Industrial applications of soft computing: a review,” Proceedings of the IEEE, vol. 89, no. 9, pp. 1243–1265, September 2001.
[33]Yardimci, “Soft computing in medicine,” Applied Soft Computing, vol. 9, no. 3, pp. 1029–1043, 2009.
[34]M. J. N. Sibley, Optical Communications: Components and Systems, 3rd ed. Springer, 2020.