Hybrid Particle Swarm Optimization for High-Dimensional Latin Hypercube Design Problem
Source abstract
Abstract
Latin Hypercube Design (LHD) is widely used in computer simulation to solve large-scale, complex, nonlinear problems. The high-dimensional LHD (HLHD) problem is one of the crucial issues and has been a large concern in the long run. This paper proposes an improved Hybrid Particle Swarm Optimization (IHPSO) algorithm to find the near-optimal HLHD by increasing the particle evolution speed and strengthening the local search. In the proposed algorithm, firstly, the diversity of the population is ensured through comprehensive learning. Secondly, the Minimum Point Distance (MPD) method is adopted to solve the oscillation problem of the PSO algorithm. Thirdly, the Ranked Ordered Value (ROV) rule is used to realize the discretization of the PSO algorithm. Finally, local and global searches are executed to find the near-optimal HLHD. The comparisons show the superiority of the proposed method compared with the existing algorithms in obtaining the near-optimal HLHD.
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Research context
OpenAlex topics: Metaheuristic Optimization Algorithms Research · Advanced Multi-Objective Optimization Algorithms · Vehicle Routing Optimization Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China
Institute of Nuclear Energy Safety Technology, China Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Science Island Branch, Graduate School of USTC, Hefei 230026, China
Funding
National Key R&D Program of China · National Natural Science Foundation of China
References (53)
Show source-provided references
1. Application of permutation genetic algorithm for sequential model building-model validation design of experiments10.1007/s00500-015-1929-5 ↗
2. Weighted Gradient-Enhanced Kriging for High-Dimensional Surrogate Modeling and Design Optimization10.2514/1.J055842 ↗
3. Adaptive residual CNN-based fault detection and diagnosis system of small modular reactors10.1016/j.asoc.2021.108064 ↗
4. Efficient space-filling and near-orthogonality sequential Latin hypercube for computer experiments10.1016/j.cma.2017.05.020 ↗
5. Design of computer experiments: A review10.1016/j.compchemeng.2017.05.010 ↗
6. Finding maximin latin hypercube designs by Iterated Local Search heuristics10.1016/j.ejor.2008.07.028 ↗
7. Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling10.1016/j.ejor.2011.05.032 ↗
8. Multicriteria Optimal Latin Hypercube Design-Based Surrogate-Assisted Design Optimization for a Permanent-Magnet Vernier Machine
9. Multivariate Time-Series Prediction in Industrial Processes via a Deep Hybrid Network Under Data Uncertainty10.1109/TII.2022.3198670 ↗
10. Maximin Latin hypercube designs in two dimensions10.1287/opre.1060.0317 ↗
11. New approach for planning out of experiments
12. A study on algorithms for optimization of Latin hypercubes10.1016/j.jspi.2005.01.007 ↗
13. Formulation of the Audze-Eglais uniform Latin hypercube design of experiments for constrained design spaces10.1016/j.advengsoft.2011.05.004 ↗
14. Exploratory designs for computational experiments10.1016/0378-3758(94)00035-T ↗
15. Algorithmic construction of optimal symmetric Latin hypercube designs10.1016/S0378-3758(00)00105-1 ↗
16. An efficient algorithm for constructing optimal design of computer experiments10.1016/j.jspi.2004.02.014 ↗
17. An algorithm for fast optimal Latin hypercube design of experiments10.1002/nme.2750 ↗
18. Centered L-2-discrepancy of random sampling and Latin hypercube design, and construction of uniform designs10.1090/S0025-5718-00-01281-3 ↗
19. Doerr, C., and De Rainville, F.M. (2013, January 11–15). Constructing Low Star Discrepancy Point Sets with Genetic Algorithms. Proceedings of the 15th Genetic and Evolutionary Computation Conference (GECCO), Madrid, Spain.10.1145/2463372.2463469 ↗
20. Evolutionary Optimization of Low-Discrepancy Sequences10.1145/2133390.2133393 ↗
21. Formulation of the Audze-Eglais Uniform Latin Hypercube design of experiments10.1016/S0965-9978(03)00042-5 ↗
22. A novel algorithm of maximin Latin hypercube design using successive local enumeration10.1080/0305215X.2011.591790 ↗
23. Optimal Sliced Latin Hypercube Designs10.1080/00401706.2014.957867 ↗
24. An efficient optimum Latin hypercube sampling technique based on sequencing optimisation using simulated annealing10.1080/00207721.2013.835003 ↗
25. The First Approximation Algorithm for the Maximin Latin Hypercube Design Problem10.1287/opre.2017.1665 ↗
26. Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the International Conference on Neural Networks (ICNN 95), Perth, WA, Australia.
27. Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application10.1016/j.swevo.2021.100868 ↗
28. Predicting the output from a complex computer code when fast approximations are available10.1093/biomet/87.1.1 ↗
29. Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives10.3390/make1010010 ↗
30. Bratton, D., and Kennedy, J. (2007, January 1–5). Defining a standard for particle swarm optimization. Proceedings of the IEEE Swarm Intelligence Symposium, Honolulu, HI, USA.10.1109/SIS.2007.368035 ↗
31. A novel nature-inspired algorithm for optimization: Squirrel search algorithm10.1016/j.swevo.2018.02.013 ↗
32. The particle swarm—Explosion, stability, and convergence in a multidimensional complex space10.1109/4235.985692 ↗
33. CoV2-Detect-Net: Design of COVID-19 prediction model based on hybrid DE-PSO with SVM using chest X-ray images10.1016/j.ins.2021.03.062 ↗
34. BNC-PSO: Structure learning of Bayesian networks by Particle Swarm Optimization10.1016/j.ins.2016.01.090 ↗
35. Optimizing Latin hypercube designs by particle swarm10.1007/s11222-012-9363-3 ↗
36. An adaptive memetic Particle Swarm Optimization algorithm for finding large-scale Latin hypercube designs10.1016/j.engappai.2014.07.021 ↗
37. A cooperative approach to particle swarm optimization10.1109/TEVC.2004.826069 ↗
38. Particle swarm optimization based on dimensional learning strategy10.1016/j.swevo.2018.12.009 ↗
39. Convergence Analysis of Particle Swarm Optimizer and Its Improved Algorithm Based on Velocity Differential Evolution10.1155/2013/384125 ↗
40. Bansal, J.C., Singh, P.K., Saraswat, M., Verma, A., Jadon, S.S., and Abraham, A. (2011, January 19–21). Inertia Weight strategies in Particle Swarm Optimization. Proceedings of the 2011 Third World Congress on Nature and Biologically Inspired Computing, Salamanca, Spain.10.1109/NaBIC.2011.6089659 ↗
41. Larsen, R.B., Jouffroy, J., and Lassen, B. (July, January 29). On the premature convergence of particle swarm optimization. Proceedings of the European Control Conference (ECC), Aalborg, Denmark.
42. Sliced Latin Hypercube Designs10.1080/01621459.2011.644132 ↗
43. Santner, T.J., Williams, B.J., and Notz, W.I. (2019). The Design and Analysis of Computer Experiments, Springer. [2nd ed.].10.1007/978-1-4939-8847-1 ↗
44. An Efficient Resource Allocation Scheme Using Particle Swarm Optimization10.1109/TEVC.2012.2185052 ↗
45. Michaloglou, A., and Tsitsas, N.L. (2021). Feasible Optimal Solutions of Electromagnetic Cloaking Problems by Chaotic Accelerated Particle Swarm Optimization. Mathematics, 9.10.3390/math9212725 ↗
46. The combined social engineering particle swarm optimization for real-world engineering problems: A case study of model-based structural health monitoring10.1016/j.asoc.2022.108919 ↗
47. Michaloglou, A., and Tsitsas, N.L. (2023). A Brain Storm and Chaotic Accelerated Particle Swarm Optimization Hybridization. Algorithms, 16.10.3390/a16040208 ↗
48. Alkayem, N.F., Shen, L., Al-hababi, T., Qian, X., and Cao, M. (2022). Inverse Analysis of Structural Damage Based on the Modal Kinetic and Strain Energies with the Novel Oppositional Unified Particle Swarm Gradient-Based Optimizer. Appl. Sci., 12.10.3390/app122211689 ↗
49. Genetic algorithms and random keys for sequencing and optimization10.1287/ijoc.6.2.154 ↗
50. Handling boundary constraints for particle swarm optimization in high-dimensional search space10.1016/j.ins.2010.11.030 ↗
51. Helwig, S., and Wanka, R. (2007, January 1–5). Particle swarm optimization in high-dimensional bounded search spaces. Proceedings of the IEEE Swarm Intelligence Symposium, Honolulu, HI, USA.10.1109/SIS.2007.368046 ↗
52. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions10.1109/TEVC.2005.857610 ↗
53. A modified comprehensive learning particle swarm optimizer and its application in cylindricity error evaluation problem10.3934/mbe.2019057 ↗