面向全局优化的种群中心引导型粒子群优化算法
Population-Center-Guided Particle Swarm Optimization Algorithm for Global Optimization
DOI: 10.12677/csa.2025.155139, PDF,   
作者: 周刘长：温州大学计算机与人工智能学院，浙江 温州
关键词: PSO；PSOSI；PSOLP；全局优化；工程问题优化；PSO； PSOSI； PSOLP； Global Optimization； Engineering Problem Optimization
摘要: 传统粒子群优化算法(PSO)在求解复杂优化问题时易陷入局部最优，限制了其全局搜索性能。为提升其全局寻优能力，本文提出面向全局优化的种群中心引导型粒子群优化算法，设计了两种改进机制：一是引入种群中心位置作为额外引导信息，构建粒子群优化–社会影响算法(PSOSI)；二是基于种群聚集程度引入扰动策略，构建粒子群优化–局部扰动算法(PSOLP)。本文在CEC-2022标准测试集及8个实际工程设计问题上对所提算法进行了系统评估。实验结果表明，PSOSI和PSOLP在优化精度和收敛稳定性方面均优于标准PSO及多种主流对比算法，验证了所提方法的有效性与通用性，为解决全局优化与工程优化问题提供了高效可行的工具。
Abstract: Traditional Particle Swarm Optimization (PSO) algorithms tend to fall into local optima when solving complex optimization problems, which hampers their global search capability. To enhance global optimization performance, this paper proposes population-center-guided PSO framework and introduces two improved variants: Particle Swarm Optimization with Social Influence (PSOSI), which incorporates the population center as an additional guiding factor, and Particle Swarm Optimization with Local Perturbation (PSOLP), which introduces a disturbance strategy based on population aggregation. The proposed algorithms are systematically evaluated on the CEC-2022 benchmark suite and eight real-world engineering design problems. Experimental results demonstrate that both PSOSI and PSOLP achieve superior optimization accuracy and convergence stability compared to standard PSO and several state-of-the-art algorithms, validating the effectiveness and generality of the proposed approaches. These methods offer practical tools for addressing global and engineering optimization tasks.
文章引用：周刘长. 面向全局优化的种群中心引导型粒子群优化算法[J]. 计算机科学与应用, 2025, 15(5): 669-679. https://doi.org/10.12677/csa.2025.155139

参考文献

[1] Kennedy, J. and Eberhart, R. (1995) Particle Swarm Optimization. Proceedings of ICNN’95—International Conference on Neural Networks, Perth, 27 November-1 December 1995, 1942-1948. [Google Scholar] [CrossRef] 
[2] Elbes, M., Alzubi, S., Kanan, T., Al-Fuqaha, A. and Hawashin, B. (2019) A Survey on Particle Swarm Optimization with Emphasis on Engineering and Network Applications. Evolutionary Intelligence, 12, 113-129. [Google Scholar] [CrossRef] 
[3] Shi, Y. and Eberhart, R. (1998) A Modified Particle Swarm Optimizer. 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360), Anchorage, 4-9 May 1998, 69-73.
[4] Shi, Y. and Eberhart, R.C. (1999) Empirical Study of Particle Swarm Optimization. Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), Washington, 6-9 July 1999, 1945-1950.
[5] Liang, J.J., Qin, A.K., Suganthan, P.N. and Baskar, S. (2004) Particle Swarm Optimization Algorithms with Novel Learning Strategies. 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), The Hague, 10-13 October 2004, 3659-3664. [Google Scholar] [CrossRef] 
[6] Li, X., & Yao, X. (2012) Cooperatively Coevolving Particle Swarms for Large Scale Optimization. IEEE Transactions on Evolutionary Computation, 16, 210-224. [Google Scholar] [CrossRef] 
[7] Wang, Y., Li, B., Weise, T., Wang, J., Yuan, B. and Tian, Q. (2011) Self-Adaptive Learning Based Particle Swarm Optimization. Information Sciences, 181, 4515-4538. [Google Scholar] [CrossRef] 
[8] Zhang, Y. (2023) Elite Archives-Driven Particle Swarm Optimization for Large Scale Numerical Optimization and Its Engineering Applications. Swarm and Evolutionary Computation, 76, Article ID: 101212. [Google Scholar] [CrossRef] 
[9] Kumar, L., Pandey, M. and Ahirwal, M.K. (2023) Parallel Global Best-Worst Particle Swarm Optimization Algorithm for Solving Optimization Problems. Applied Soft Computing, 142, 110329. [Google Scholar] [CrossRef] 
[10] Qaraad, M., Amjad, S., Hussein, N.K., Farag, M.A., Mirjalili, S. and Elhosseini, M.A. (2024) Quadratic Interpolation and a New Local Search Approach to Improve Particle Swarm Optimization: Solar Photovoltaic Parameter Estimation. Expert Systems with Applications, 236, Article ID: 121417. [Google Scholar] [CrossRef] 
[11] Yazdani, D., Branke, J., Omidvar, M. N., Li, X., Li, C., Mavrovouniotis, M., Yao, X., et al. (2021) IEEE CEC 2022 Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark. arXiv: 2106.06174.
[12] Tzanetos, A. and Blondin, M. (2023) A Qualitative Systematic Review of Metaheuristics Applied to Tension/Compression Spring Design Problem: Current Situation, Recommendations, and Research Direction. Engineering Applications of Artificial Intelligence, 118, Article ID: 105521. [Google Scholar] [CrossRef] 
[13] Hassan, S., Kumar, K., Raj, C.D. and Sridhar, K. (2013) Design and Optimisation of Pressure Vessel Using Metaheuristic Approach. Applied Mechanics and Materials, 465, 401-406. [Google Scholar] [CrossRef] 
[14] Erdogan Yildirim, A. and Karci, A. (2018) Application of Three Bar Truss Problem among Engineering Design Optimization Problems Using Artificial Atom Algorithm. 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, 28-30 September 2018, 1-5. [Google Scholar] [CrossRef] 
[15] Alkurdi, A.A. (2023) Optimization of Welded Beam Design Problem Using Water Evaporation Optimization Algorithm. Academic Journal of Nawroz University, 12, 499-509. [Google Scholar] [CrossRef] 
[16] Lin, M., Tsai, J., Hu, N. and Chang, S. (2013) Design Optimization of a Speed Reducer Using Deterministic Techniques. Mathematical Problems in Engineering, 2013, Article ID: 419043. [Google Scholar] [CrossRef] 
[17] Panda, S., Panda, S.N., Nanda, P. and Mishra, D. (2015) Comparative Study on Optimum Design of Rolling Element Bearing. Tribology International, 92, 595-604. [Google Scholar] [CrossRef] 
[18] Yücel, M., Bekdaş, G. and Ni̇gdeli̇, S.M. (2020) Minimizing the Weight of Cantilever Beam via Metaheuristic Methods by Using Different Population-Iteration Combinations. WSEAS Transactions on Computers, 19, 69-77. [Google Scholar] [CrossRef] 
[19] Đurđev, M., Milošević, M., Lukić, D., Antić, A., Novaković, B. and Đorđević, L. (2023) Gauss-Based Honey Badger Algorithm for Step-Cone Pulley Optimization Problem. In: Karabegovic, I., Kovačević, A. and Mandzuka, S., Eds., New Technologies, Development and Application VI. NT 2023, Springer, 78-85. [Google Scholar] [CrossRef] 
[20] Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M. and Chen, H. (2019) Harris Hawks Optimization: Algorithm and Applications. Future Generation Computer Systems, 97, 849-872. [Google Scholar] [CrossRef] 
[21] Falahah, I.A., Al-Baik, O., Alomari, S., Bektemyssova, G., Gochhait, S., Leonova, I., et al. (2024) Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications. Computers, Materials & Continua, 79, 3631-3678. [Google Scholar] [CrossRef] 
[22] Oladejo, S.O., Ekwe, S.O. and Mirjalili, S. (2024) The Hiking Optimization Algorithm: A Novel Human-Based Metaheuristic Approach. Knowledge-Based Systems, 296, Article ID: 111880. [Google Scholar] [CrossRef] 
[23] Mirjalili, S. (2016) SCA: A Sine Cosine Algorithm for Solving Optimization Problems. Knowledge-Based Systems, 96, 120-133. [Google Scholar] [CrossRef] 
[24] Mirjalili, S. and Lewis, A. (2016) The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67. [Google Scholar] [CrossRef] 
[25] Wilcoxon, F. (1992) Individual Comparisons by Ranking Methods. In: Kotz, S. and Johnson, N.L., Eds., Breakthroughs in Statistics, Springer, 196-202. [Google Scholar] [CrossRef] 
[26] Friedman, M. (1937) The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance. Journal of the American Statistical Association, 32, 675-701. [Google Scholar] [CrossRef]