Advanced PSO Algorithms Development with Combined lbest and gbest Neighborhood Topologies

Yuriy, Romasevych; Viatcheslav, Loveikin; Ziv, Brand

doi:10.2478/cait-2024-0025

Abstract

This paper introduces an innovative approach integrating global best (gbest) and local best (lbest) PSO communication topologies. The algorithm initiates with lbest and seamlessly transitions to gbest, with the switching rate controlled by the parameter “a”. Rational values of “a” is determined through numerical experiments. A comparative methodology employing two estimation criteria is used to showcase the improved performance of the modified PSO-based algorithms. Furthermore, the efficacy of this approach is demonstrated in addressing two optimal control problems within dynamical systems. Results highlight the modified algorithms’ superiority in terms of the total number of successful runs and statistical indicators. Consequently, these advanced algorithms prove effective for applications such as artificial neural network training, controller gains determination, and similar problem domains.

References

Kennedy, J., R. C. Eberhart. Particle Swarm Optimization. – In: Proc. of IEEE International Conference on Neural Networks, 1995, pp. 1942-1948.
Search in Google Scholar Back to article
Houssein, E. H., A. G. Gad, K. Hussain, P. N. Suganthan. Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application. – Swarm and Evolutionary Computation, Vol. 63, 2021, 100868. DOI: 10.1016/j.swevo.2021.100868.
Search in Google Scholar Back to article
Freitas, D., L. G. Lopes, F. Morgado-Dias. Particle Swarm Optimisation: A Historical Review Up to the Current Developments. – Entropy, Vol. 22, 2020, No 3, 362. DOI: 10.3390/e22030362.
Search in Google Scholar Back to article
Blackwell, T., J. Kennedy. Impact of Communication Topology in Particle Swarm Optimization. – IEEE Transactions on Evolutionary Computation, Vol. 23, 2019, No 4, pp. 689-702. DOI: 10.1109/tevc.2018.2880894.
Search in Google Scholar Back to article
Kennedy, J., R. Mendes (n.d.). Population Structure and Particle Swarm Performance. – In: Proc. of Congress on Evolutionary Computation (CEC’02), 2002 (Cat. No 02TH8600). DOI: 10.1109/cec.2002.1004493.
Search in Google Scholar Back to article
Engelbrecht, A. P. Particle Swarm Optimization: Global Best or Local Best? – In: Proc. of BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, 2013. DOI: 10.1109/brics-cci-cbic.2013.31.
Search in Google Scholar Back to article
Liu, H., B. Li, Y. Ji, T. Sun (n.d.). Particle Swarm Optimisation from lbest to gbest. – Applied Soft Computing Technologies: The Challenge of Complexity, pp. 537-545. DOI: 10.1007/3-540-31662-0_41.
Search in Google Scholar Back to article
Vazquez, J. C., F. Valdez, P. Melin. Comparative Study of Social Network Structures in PSO. – Recent Advances on Hybrid Approaches for Designing Intelligent Systems, 2014, pp. 239-254. DOI: 10.1007/978-3-319-05170-3_17.
Search in Google Scholar Back to article
Ahmed, G. G. Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review. – Archives of Computational Methods in Engineering, Vol. 29, 2022, pp. 2531-2561. DOI: 10.1007/s11831-021-09694-4.
Search in Google Scholar Back to article
Shami, T. M., A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh, S. Mirjalili. Particle Swarm Optimization: A Comprehensive Survey. – IEEE Access, Vol. 10, 2022, pp. 10031-10061. DOI: 10.1109/ACCESS.2022.3142859
Search in Google Scholar Back to article
Houssein, E. H., A. G. Gad, K. Hussain, P. N. Suganthan. Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application. – Swarm and Evolutionary Computation, Vol. 63, 2021, 100868. DOI: 10.1016/j.swevo.2021.100868.
Search in Google Scholar Back to article
Yudong, Z., W. Shuihua, J. Genlin. A Comprehensive Survey on Particle Swarm Optimization. Algorithm and Its Applications. – Mathematical Problems in Engineering, Vol. 38, 2015, 931256. DOI: org/10.1155/2015/931256.
Search in Google Scholar Back to article
Pluhacek, M., A. Kazikova, T. Kadavy, A. Viktorin, R. Senkerik. Relation of Neighborhood Size and Diversity Loss Rate in Particle Swarm Optimization with Ring Topology. Mendel. – Soft Computing Journal, Vol. 27, 2021, No 2, pp. 74-79. DOI: 10.13164/mendel.2021.k.0d9.
Search in Google Scholar Back to article
Ni, J. C., L. Li, F. Qiao. A Topology Based on a Local World Evolving Model for PSO. – Advanced Materials Research, Vol. 219-220, 2011, pp. 1297-1300. DOI: 10.4028/www.scientific.net/amr.219-220.1297.
Search in Google Scholar Back to article
Ghosh, S., D. Kundu, K. Suresh, S. Das, A. Abraham, B. K. Panigrahi, V. Snasel. On Some Properties of the lbest Topology in Particle Swarm Optimization. – In: Proc. of 9th International Conference on Hybrid Intelligent Systems, 2009. DOI: 10.1109/his.2009.288.
Search in Google Scholar Back to article
Fernandes, C. M., A. C. Rosa, J. L. J. Laredo, C. Cotta, J. J. Merelo. A Study on Time-Varying Partially Connected Topologies for the Particle Swarm. – In: Proc. of IEEE Congress on Evolutionary Computation, 2013. DOI: 10.1109/cec.2013.6557863.
Search in Google Scholar Back to article
Tsujimoto, T., T. Shindo, T. Kimura, K. Jin’no. A Relationship between Network Topology and Search Performance of PSO. – In: Proc. of IEEE Congress on Evolutionary Computation. 2,012. DOI: 10.1109/cec.2012.6256536.
Search in Google Scholar Back to article
Suganthan, P. N. Particle Swarm Optimiser with Neighbourhood Operator. – In: Proc. of Congress on Evolutionary Computation-CEC99 (Cat. No 99TH8406), Washington, DC, USA, Vol. 3, 1999, pp. 1958-1962. DOI: 10.1109/CEC.1999.785514.
Search in Google Scholar Back to article
Kennedy, J. Stereotyping: Improving Particle Swarm Performance with Cluster Analysis. – In: Proc. of Congress on Evolutionary Computation. CEC00 (Cat. No 00TH8512), La Jolla, CA, USA, Vol. 2, 2000, pp. 1507-1512. DOI: 10.1109/CEC.2000.870832.
Search in Google Scholar Back to article
Parsopoulus, K. E., N. M. Vrahatis. UPSO: A Unified Particle Swarm Optimization Scheme. – Lecture Series on Computational Siences, 2004, No 1, pp. 868-873.
Search in Google Scholar Back to article
Marinakis, Y., A. Migdalas, A. Sifaleras. A Hybrid Particle Swarm Optimization – Variable Neighborhood Search Algorithm for Constrained Shortest Path Problems. – European Journal of Operational Research, Vol. 261, 2017, No 3, pp. 819-834. DOI: 10.1016/j.ejor.2017.03.031.
Search in Google Scholar Back to article
Wei, S., L. Anping, Y. Hongshan, L. Qiaokang, W. Guohua. All-Dimension Neighborhood Based Particle Swarm Optimization with Randomly Selected Neighbors. – Information Sciences, 2017, pp. 1-22. DOI: 10.1016/j.ins.2017.04.007.
Search in Google Scholar Back to article
H. Ishibuchi, Q. Zhang, R. Cheng, K. Li, H. Li, H. Wang, A. Zhou, Eds. Evolutionary Multi-Criterion Optimization. – In: Lecture Notes in Computer Science. 2021. DOI:10.1007/978-3-030-72062-9.
Search in Google Scholar Back to article
Lynn, N., M. Z. Ali, P. N. Suganthan. Population Topologies for Particle Swarm Optimization and Differential Evolution. – Swarm and Evolutionary Computation, Vol. 39, 2018, pp. 24-35. DOI: 10.1016/j.swevo.2017.11.002.
Search in Google Scholar Back to article
Li, X. Niching without Niching Parameters: Particle Swarm Optimization Using a Ring Topology. – IEEE Transactions on Evolutionary Computation, Vol. 14, 2010, No 1, pp. 150-169, 5352335. DOI: 10.1109/TEVC.2009.2026270.
Search in Google Scholar Back to article
Romasevych, Y., V. Loveikin, Y. Loveikin. Development of New Rotating Ring Topology of PSO-Algorithm. – In: Proc. of 2nd IEEE KhPI Week on Advanced Technology (KhPIWeek’21), Kharkiv, Ukraine, 2021, pp. 79-82. DOI: 10.1109/KhPIWeek53812.2021.9569973.
Search in Google Scholar Back to article
Storn, R., K. Price. Differential Evolution – A Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces. – Journal of Global Optimization, Vol. 11, 1997, No 4, pp. 341-359.
Search in Google Scholar Back to article
Optimization & Eye Pleasure: 78 Benchmark Test Functions for Single Objective Optimization. https://towardsdatascience.com/optimization-eye-pleasure-78-benchmark-test-functions-for-single-objective-optimization-92e7ed1d1f12
Search in Google Scholar Back to article
Virtual Library of Simulation Experiments: Test Functions and Datasets. Optimization Test Problems. https://www.sfu.ca/~ssurjano/optimization.html
Search in Google Scholar Back to article
HappyCat – A Simple Function Class Where Well-Known Direct Search Algorithms Do Fail. https://homepages.fhv.at/hgb/New-Papers/PPSN12_BF12.pdf
Search in Google Scholar Back to article
Momin, J., Y. Xin-She. A Literature Survey of Benchmark Functions for Global Optimization Problems. – Int. Journal of Mathematical Modelling and Numerical Optimisation, Vol. 4, 2013, No 2, pp. 150-194. DOI: 10.1504/IJMMNO.2013.055204.
Search in Google Scholar Back to article
Montaz, A. M., C. Khompatraporn, Z. B. Zabinsky. A Numerical Evaluation of Several Stochastic Algorithms on Selected Continuous Global Optimization Test Problems. – Journal of Global Optimization, Vol. 31, 2005, pp. 635-672. DOI: 10.1007/s10898-004-9972-2.
Search in Google Scholar Back to article
Romasevych, Y., V. Loveikin, V. Makarets. Optimal Constrained Tuning of PI-Controllers via a New PSO-Based Technique. – International Journal of Swarm Intelligence Research, Vol. 11, 2020, No 4, pp. 87-105. DOI: 10.4018/IJSIR.2020100104.
Search in Google Scholar Back to article
Shi, Y., R. Eberhart. A Modified Particle Swarm Optimizer. – In: Proc. of IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence, 1998, pp. 69-73. DOI: 10.1109/ICEC.1998.699146.
Search in Google Scholar Back to article
Romasevych, Y., V. Loveikin, Y. Loveikin. Development of a PSO Modification with Varying Cognitive Term. – In: Proc. of 3rd IEEE KhPI Week on Advanced Technology (KhPIWeek), IEEE, 2022, pp. 55-59. DOI: 10.1109/KhPIWeek57572.2022.9916413.
Search in Google Scholar Back to article
Article Additional Data. https://drive.google.com/file/d/1Oj5-GzgczntCWHDRYazkIhjvTkpHTzW8/view?usp=sharing
Search in Google Scholar Back to article
Romasevych, Y., V. Loveikin, M. Ohiienko, L. Shymko, K. Łukawiecki. Innovation Management in Agriculture. Agrotronics and Design of Optimal Controllers Based on New Modifications of Particle Swarm Optimization, 2021. https://www.wszia.opole.pl/wp-content/uploads/2020/09/Mon_Romasevich.pdf
Search in Google Scholar Back to article
Cazzolato, B. S., Z. Prime. On the Dynamics of the Furuta Pendulum. – Journal of Control Science and Engineering, 2011, Article ID 528341. 8 p. DOI: 10.1155/2011/528341.
Search in Google Scholar Back to article
Antonio-Cruz, M., V. M. Hernandez-Guzman, C. A. Merlo-Zapata, C. Marquez-Sanchez. Nonlinear Control with Friction Compensation to Swing-Up a Furuta Pendulum. – ISA Transactions, Vol. 139, 2023, pp. 713-723. DOI: 10.1016/j.isatra.2023.05.007.
Search in Google Scholar Back to article
Dallali, H., P. Kormushev, Z. Li, D. Caldwell. On Global Optimization of Walking Gaits for the Compliant Humanoid Robot, COMAN Using Reinforcement Learning. – Cybernetics and Information Technologies, Vol. 12, 2012, No 3, pp. 39-52.
Search in Google Scholar Back to article
Huang, Z., F. Huang, X. Wang, F. Chu. Active Vibration Control of Composite Cantilever Beams. – Materials, Vol. 16, 2023, 95. DOI: 10.3390/ma16010095.
Search in Google Scholar Back to article
Huang, Z., Y. Mao, A. Dai, M. Han, X. Wang, F. Chu. Active Vibration Control of Piezoelectric Sandwich Plates. – Materials, Vol. 15, 2022, 3907. DOI: 10.3390/ma15113907.
Search in Google Scholar Back to article
Xin, Z., D. Gao, S. Lu, X. Fu, Y. Zhu, J. Xu. Research of Active Vibration Control for Cantilever Beam Based on Macro Fiber Composite Actuators. – In: Proc. of International Conference on Cyber-Physical Social Intelligence (ICCSI’22), Nanjing, China, 2022, pp. 573-577. DOI: 10.1109/ICCSI55536.2022.9970636.
Search in Google Scholar Back to article

Advanced PSO Algorithms Development with Combined lbest and gbest Neighborhood Topologies

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