Open Access
Issue
JNWPU
Volume 42, Number 5, October 2024
Page(s) 948 - 958
DOI https://doi.org/10.1051/jnwpu/20244250948
Published online 06 December 2024
  1. SHEN C, ZHANG K. Two-stage improved grey wolf optimization algorithm for feature selection on high-dimensional classification[J]. Complex & Intelligent Systems, 2021, 8(4): 2769–2789 [Google Scholar]
  2. OU X F, WU M, PU Y Y, et al. Cuckoo search algorithm with fuzzy logic and Gauss-Cauchy for minimizing localization error of WSN[J]. Applied Soft Computing, 2022, 125: 109211. [Article] [CrossRef] [Google Scholar]
  3. MONTALVO I, IZQUIERDO J, PÉREZ R, et al. A diversity-enriched variant of discrete PSO applied to the design of water distribution networks[J]. Engineering Optimization, 2008, 40(7): 655–668. [Article] [CrossRef] [Google Scholar]
  4. GONG Y J, ZHANG J, CHUNG S H, et al. An efficient resource allocation scheme using particle swarm optimization[J]. IEEE Trans on Evolutionary Computation, 2012, 16(6): 801–816. [Article] [CrossRef] [Google Scholar]
  5. ZHANG H, XIE J, GE J, et al. An entropy-based PSO for DAR task scheduling problem[J]. Applied Soft Computing, 2018, 73: 862–873. [Article] [CrossRef] [Google Scholar]
  6. GOLDBERG D E. Genetic algorithms in search, optimization, and machine learning[M]. Boston: Adottion-Wesley Professional, 1989 [Google Scholar]
  7. YANG J, YANG S, NI P. A vector tabu search algorithm with enhanced searching ability for pareto solutions and its application to multi-objective optimizations[J]. IEEE Trans on Magnetics, 2016, 52(3): 1–4 [NASA ADS] [Google Scholar]
  8. MARTINS D, MENDES M. A Hybrid evolutionary approach applied to the economic dispatch problem with prohibited operating zones and uncertainties[J]. Latin America Transactions, 2021, 19(7): 1225–1232. [Article] [CrossRef] [Google Scholar]
  9. ZHAN Z H, ZHANG J, YUN L, et al. Adaptive particle swarm optimization[J]. IEEE Trans on Systems, Man, and Cybernetics, Part B: Cybernetics, 2009, 39(6): 1362–1381. [Article] [CrossRef] [Google Scholar]
  10. LIU Q, DU S, WYK B J V, et al. Niching particle swarm optimization based on euclidean distance and hierarchical clustering for multimodal optimization[J]. Nonlinear Dynamics, 2020, 99(1): 1–19. [Article] [CrossRef] [Google Scholar]
  11. KENNEDY J, EBERHART R. Particle swarm otimization[C]//International Conference on Neural Networks, 1995 [Google Scholar]
  12. EBERHART R, KENNEDY J. A new optimizer using particle swarm theory[C]//Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995: 39–43 [Google Scholar]
  13. SHI Y. Parameter selection in particle swarm optimization[J]. Evolutionary Programming, 1998, 1447: 591–600 [CrossRef] [Google Scholar]
  14. CAO Y, ZHANG H, LI W, et al. Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions[J]. IEEE Trans on Evolutionary Computation, 2019, 23(4): 718–731. [Article] [CrossRef] [Google Scholar]
  15. YU W, LI B, WEISE T, et al. Self-adaptive learning based particle swarm optimization[J]. Information Sciences, 2011, 181(20): 4515–4538. [Article] [CrossRef] [Google Scholar]
  16. YU F, TONG L, XIA X W. Adjustable driving force based particle swarm optimization algorithm[J]. Information Sciences, 2022, 609: 60–78. [Article] [CrossRef] [Google Scholar]
  17. SHI Y, EBERHART R. A modified particle swarm optimizer[C]//1998 IEEE International Conference on Evolutionary Computation, 1998: 69–73 [Google Scholar]
  18. CLERC M, KENNEDY J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space[J]. IEEE Trans on Evolutionary Computation, 2002, 6(1): 58–73. [Article] [CrossRef] [Google Scholar]
  19. RATNAWEERA A, HALGAMUGE S K, WATSON H C. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J]. IEEE Trans on Evolutionary Computation, 2004, 8(3): 240–255. [Article] [CrossRef] [Google Scholar]
  20. LIU W, WANG Z, YUAN Y, et al. A novel sigmoid-function-based adaptive weighted particle swarm optimizer[J]. IEEE Trans on Cybernetics, 2021, 51(2): 1085–1093. [Article] [CrossRef] [Google Scholar]
  21. RUI M, KENNEDY J, NEVES J. The fully informed particle swarm: simpler, maybe better[J]. IEEE Trans on Evolutionary Computation, 2004, 8(3): 204–210. [Article] [CrossRef] [Google Scholar]
  22. LIANG J J, QIN A K, SUGANTHAN P N, et al. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J]. IEEE Trans on Evolutionary Computation, 2006, 10(3): 281–295. [Article] [CrossRef] [Google Scholar]
  23. YE W, FENG W, FAN S. A novel multi-swarm particle swarm optimization with dynamic learning strategy[J]. Applied Soft Computing, 2017, 61: 832–843. [Article] [CrossRef] [Google Scholar]
  24. ZENG N Y, WANG Z D, LIU W B, et al. A dynamic neighborhood-based switching particle swarm optimization algorithm[J]. IEEE Trans on Cybernetics, 2020, 50(1): 1–12. [Article] [Google Scholar]
  25. CHENG R, JIN Y. A social learning particle swarm optimization algorithm for scalable optimization[J]. Information Sciences, 2015, 291: 43–60. [Article] [CrossRef] [Google Scholar]
  26. LI W, MENG X, HUANG Y, et al. Multi-population cooperative particle swarm optimization with a mixed mutation strategy[J]. Information Sciences, 2020, 529: 179–196. [Article] [CrossRef] [Google Scholar]
  27. HUANG Yi, LIANG Fangchi, FAN Chengli, et al. Particle swarm optimization algorithm with random mutation and perception factor[J]. Journal of Northwestern Polytechnical University, 2023, 41(2): 428–438 (in Chinese) [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  28. KUMAR N, VIDYARTHI D P. A novel hybrid PSO-GA meta-heuristic for scheduling of DAG with communication on multiprocessor systems[J]. Engineering with Computers, 2016, 32(1): 35–47. [Article] [CrossRef] [Google Scholar]
  29. JAVIDRAD F, NAZARI M. A new hybrid particle swarm and simulated annealing stochastic optimization method[J]. Applied Soft Computing, 2017, 60: 634–654. [Article] [CrossRef] [Google Scholar]
  30. THARWAT A, ELHOSENY M, HASSANIEN A E, et al. Intelligent Bézier curve-based path planning model using chaotic particle swarm optimization algorithm[J]. Cluster Computing, 2019, 22(4): 1–22 [CrossRef] [Google Scholar]
  31. ZHANG R, CHANG P C, SONG S, et al. Local search enhanced multi-objective PSO algorithm for scheduling textile production processes with environmental considerations[J]. Applied Soft Computing, 2017, 61(1): 447–467 [CrossRef] [Google Scholar]
  32. WANG H M, QIAO Z W, XIA C L, et al. Self-regulating and self-evolving particle swarm optimizer[J]. Engineering Optimization, 2015, 47(1): 129–147. [Article] [CrossRef] [Google Scholar]
  33. JAMES F. Chaos and randomness[J]. Chaos Solitons & Fractals, 1995, 6: 221–226 [CrossRef] [Google Scholar]
  34. ZHANG M, LONG D, QIN T, et al. A chaotic hybrid butterfly optimization algorithm with particle swarm optimization for high-dimensional optimization problems[J]. Symmetry, 2020, 12(11): 1800 [CrossRef] [Google Scholar]
  35. XIA X W, GUI L, HE G L, et al. An expanded particle swarm optimization based on multi-exemplar and forgetting ability[J]. Information Sciences: an International Journal, 2020, 508: 105–120 [CrossRef] [Google Scholar]
  36. VESTERSTROM J, THOMSEN R. A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems[C]//Proceedings of the 2004 Congress on Evolutionary Computation, 2004: 1980–1987 [Google Scholar]
  37. YAN Qunmin, MA Ruiqing, MA Yongxiang, et al. Adaptive simulated annealing particle swarm optimization algorithm[J]. Journal of Xidian University, 2021, 48(4): 8 (in Chinese) [Google Scholar]
  38. LU Fuyu, TONG Ningning, FENG Weike, et al. Adaptive hybrid annealing particle swarm optimization algorithm[J]. Systems Engineering and Electronics, 2022, 44(11): 3470–3476 (in Chinese) [Google Scholar]
  39. JIANG J, LI X, DENG Z H, et al. Control-oriented dynamic model optimization of steam reformer with an improved optimization algorithm[J]. International Journal of Hydrogen Energy, 2013, 38(26): 11288–11302 [CrossRef] [Google Scholar]
  40. RAN C, JIN Y, et al. A social learning particle swarm optimization algorithm for scalable optimization[J]. Information Sciences an International Journal, 2015, 291: 43–60 [CrossRef] [Google Scholar]
  41. HAN H, WU X, ZHANG L, et al. Self-organizing RBF neural network using an adaptive gradient multiobjective particle swarm optimization[J]. IEEE Trans on Cybernetics, 2019, 49(1): 69–82 [CrossRef] [Google Scholar]
  42. LIU S, WANG Z, WEI G, et al. Distributed set-membership filtering for multi-rate systems under the round-robin scheduling over sensor networks[J]. IEEE Trans on Cybernetics, 2020, 50(5): 1910–1920 [CrossRef] [Google Scholar]
  43. ZOU, LEI, WANG, et al. Recursive filtering for time-varying systems with random access protocol[J]. IEEE Trans on Automatic Control, 2019, 64(2): 720–727 [Google Scholar]
  44. ZOU L, WANG Z, HAN Q L, et al. Moving horizon estimation for networked time-delay systems under round-robin protocol[J]. IEEE Trans on Automatic Control, 2019, 64(12): 5191–5198 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.