Keywords
Abstract
Particle Swarm Optimization (PSO) and Genetic algorithm (GA) are widely used in real life and are constantly improved and integrated. Both of them have their own advantages in different fields. Both of them have their own advantages in different fields. It performs exceptionally well in solving complex nonlinear problems. This article will conduct a systematic analysis from the perspectives of algorithm principles, performance comparison, and application scenarios, and also explore the future development trends by combining real-world cases.
References
[1] Abibou, S., Bourakadi, E. D., & Yahyaouy, A. (2024). Optimizing hydrogen refueling station recommendations: A comparative analysis between GA and PSO. SN Computer Science, 5(8), 991. https://doi.org/10.1007/s42979-024-02946-7
[2] Anwar, T. Q., & Vilas, W. (2023). Wind farm layout optimization through optimal wind turbine placement using a hybrid PSO and GA. Environmental Science and Pollution Research, 30(31), 77436–77452. https://doi.org/10.1007/s11356-023-27901-6
[3] Deng, L., & Liu, S. (2025). Collective dynamics of particle swarm optimization: A network science perspective. Physica A: Statistical Mechanics and its Applications, 675, 130778.
[4] Gao, W. (2025). Application of improved particle swarm optimization algorithm combined with genetic algorithm in shear wall design. Systems and Soft Computing, 7, 200198.
[5] Gupta, N. S., & Chak, K. S. (2024). Experimental investigations and optimization of surface roughness using response surface methodology coupled with GA and PSO techniques in grinding of Inconel 718. International Journal of Precision Engineering and Manufacturing, 25(12), 2437–2453. https://doi.org/10.1007/s12541-024-00998-6
[6] Gyan, S., & K., A. C. (2023). Hybrid modified PSO with GA based workflow scheduling in cloud-fog environment for multi-objective optimization. Cluster Computing, 27(2), 1947–1964. https://doi.org/10.1007/s10586-023-04033-7
[7] Hu, W., Zhang, Y., Liu, L., et al. (2024). Study on multi-objective optimization of construction project based on improved GA and PSO. Processes, 12(8), 1737. https://doi.org/10.3390/pr12081737
[8] Huber, K., Wirtz, T., & Hoang, Q. H. (2024). CPOpt: A modular framework for GA optimization and post-optimization analysis in complex charged particle optical design. Nuclear Instruments and Methods in Physics Research, Section A, 1067, 169702.
[9] Jin, Y., Xuerong, C., & Juan, L. (2021). Particle filter algorithm optimized by genetic algorithm combined with particle swarm optimization. Procedia Computer Science, 187, 206–211. https://doi.org/10.1016/j.procs.2021.04.044
[10] Kheirdast, A., Jozi, A. S., & Rezaian, S. (2024). Comparing the performance of GA and PSO algorithm in allocating and scheduling fire stations. International Journal of Environmental Science and Technology, 22(1), 445–458. https://doi.org/10.1007/s13762-023-04957-y
[11] Laishram, A., Padmanabhan, V., & Lal, P. R. (2018). Analysis of similarity measures in user-item subgroup based collaborative filtering via genetic algorithm. International Journal of Information Technology, 10(4), 523–527. https://doi.org/10.1007/s41870-018-0157-5
[12] P, G. M. S., K, N. H. N., Jain, K. A., et al. (2025). Convergence of improved PSO based ensemble model and explainable AI for accurate detection of food adulteration in red chilli powder. Journal of Food Composition and Analysis, 143, 107577.
[13] Said, G. A., & Jawale, D. (2025). Design of an iterative method for enhanced disease detection and feature selection using particle swarm optimization, genetic algorithms, and convolutional neural networks. Evolving Systems, 16(3), 95.
[14] Saini, M., Sinwar, D., Swarith, M. A., et al. (2023). Reliability and maintainability optimization of load haul dump machines using GA and PSO. Journal of Quality in Maintenance Engineering, 29(2), 356–376. https://doi.org/10.1108/JQME-06-2021-0045
[15] Seghier, B. A. E. M., Carvalho, H., & Keshtegar, B. (2020). Novel hybridized adaptive neuro‐fuzzy inference system models based particle swarm optimization and genetic algorithms for accurate prediction of stress intensity factor. Fatigue & Fracture of Engineering Materials & Structures, 43(11), 2653–2667. https://doi.org/10.1111/ffe.13325
[16] Shui, P., Zhang, Z., Xie, C., et al. (2025). Optimization of environmental conditioning equipment based on PSO algorithm and GA. Frontiers in Computing and Intelligent Systems, 11(1), 85–88.
[17] Gyan, S., & K., A. C. (2023). Hybrid modified PSO with GA (GA) based workflow scheduling in cloud-fog environment for multi-objective optimization. Cluster Computing, 27(2), 1947–1964. https://doi.org/10.1007/s10586-023-04033-7
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2025 Chen Lin, Shen Mengmeng