CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Particle Swarm Optimization: Technique, System and Challenges

by Dian Palupi Rini, Siti Mariyam Shamsuddin, Siti Sophiyati Yuhaniz
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 14 - Number 1
Year of Publication: 2011
Authors: Dian Palupi Rini, Siti Mariyam Shamsuddin, Siti Sophiyati Yuhaniz
10.5120/1810-2331

Dian Palupi Rini, Siti Mariyam Shamsuddin, Siti Sophiyati Yuhaniz . Particle Swarm Optimization: Technique, System and Challenges. International Journal of Computer Applications. 14, 1 ( January 2011), 19-27. DOI=10.5120/1810-2331

@article{ 10.5120/1810-2331,
author = { Dian Palupi Rini, Siti Mariyam Shamsuddin, Siti Sophiyati Yuhaniz },
title = { Particle Swarm Optimization: Technique, System and Challenges },
journal = { International Journal of Computer Applications },
issue_date = { January 2011 },
volume = { 14 },
number = { 1 },
month = { January },
year = { 2011 },
issn = { 0975-8887 },
pages = { 19-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume14/number1/1810-2331/ },
doi = { 10.5120/1810-2331 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:02:19.886002+05:30
%A Dian Palupi Rini
%A Siti Mariyam Shamsuddin
%A Siti Sophiyati Yuhaniz
%T Particle Swarm Optimization: Technique, System and Challenges
%J International Journal of Computer Applications
%@ 0975-8887
%V 14
%N 1
%P 19-27
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behaviors of birds flocking or fish schooling. A number of basic variations have been developed due to improve speed of convergence and quality of solution found by the PSO. On the other hand, basic PSO is more appropriate to process static, simple optimization problem. Modification PSO is developed for solving the basic PSO problem. The observation and review 46 related studies in the period between 2002 and 2010 focusing on function of PSO, advantages and disadvantages of PSO, the basic variant of PSO, Modification of PSO and applications that have implemented using PSO. The application can show which one the modified or variant PSO that haven’t been made and which one the modified or variant PSO that will be developed.

References
  1. A. P. Engelbrecht, Fundamental of Computational Swarm Inteligent, First ed. The atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England: John Wiley & Sons Ltd, 2005.
  2. B. Santosa, "Tutorial Particle Swarm Optimization," 2006.
  3. M. B. Ghalia, "Particle Swarm Optimization with an Improved Exploration-Exploitation Balance," iEEE, vol. 978-1-4244-2167-1/08/$25.00 ©2008 IEEE, 2008.
  4. Q. Bai, "Analysis of Particle Swarm Optimization Algorithm," Computer and Information Science, vol. volume 3 No 1, Pebruari 2010 2010.
  5. F. Shahzad, et al., "Opposition-Based Particle Swarm Optimization with Velocity Clamping (OVCPSO), ," Journal advances in computational Intelligent, AISC 61, pp, 339-2348, 2009.
  6. M. Ben Ghalia, "Particle swarm optimization with an improved exploration-exploitation balance," in Circuits and Systems, 2008. MWSCAS 2008. 51st Midwest Symposium on, 2008, pp. 759-762.
  7. A. Chatterjee and P. Siarry, "Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization," Computers & Operations Research, vol. 33, pp. 859-871, 2006.
  8. L. Yufeng, "Dynamic Particle Swarm Optimization Algorithm for Resolution of Overlapping Chromatograms," in Natural Computation, 2009. ICNC '09. Fifth International Conference on, 2009, pp. 246-250.
  9. S. Xianjun, et al., "A Dynamic Adaptive Particle Swarm Optimization for Knapsack Problem," in Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on, 2006, pp. 3183-3187.
  10. P. K. Tripathi, et al., "Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients," Information Sciences, vol. 177, pp. 5033-5049, 2007.
  11. K. T. Chaturvedi, et al., "Particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch," International Journal of Electrical Power & Energy Systems, vol. 31, pp. 249-257, 2009.
  12. P. Boonyaritdachochai, et al., "Optimal congestion management in an electricity market using particle swarm optimization with time-varying acceleration coefficients," Computers & Mathematics with Applications, vol. In Press, Corrected Proof, 2010.
  13. A. Engelbrecht, "particle Swarm Optimization : Pitfalls and convergen aspect."
  14. V. Kalivarapu, et al., "Synchronous parallelization of Particle Swarm Optimization with digital pheromones," Advances in Engineering Software, vol. 40, pp. 975-985, 2009.
  15. S. B. Akat and V. Gazi, "Decentralized asynchronous particle swarm optimization," in Swarm Intelligence Symposium, 2008. SIS 2008. IEEE, 2008, pp. 1-8.
  16. V. Gazi, "Asynchronous Particle Swarm Optimization," in Signal Processing and Communications Applications, 2007. SIU 2007. IEEE 15th, 2007, pp. 1-4.
  17. I. Scriven, et al., "Asynchronous multiple objective particle swarm optimisation in unreliable distributed environments," in Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on, 2008, pp. 2481-2486.
  18. W. Bo, et al., "Distributed Rate Allocation and Performance Optimization for Video Communication Over Mesh Networks," in Image Processing, 2007. ICIP 2007. IEEE International Conference on, 2007, pp. VI - 501-VI - 504.
  19. Q. Liguo, et al., "Design and Implementation of Intelligent PID Controller Based on FPGA," in Natural Computation, 2008. ICNC '08. Fourth International Conference on, 2008, pp. 511-515.
  20. T. Desell, et al., "Robust Asynchronous Optimization for Volunteer Computing Grids," in e-Science, 2009. e-Science '09. Fifth IEEE International Conference on, 2009, pp. 263-270.
  21. L. T. Bui, et al., "A Modified Strategy for the Constriction Factor in Particle Swarm Optimization," in Book Series Lecture Notes in Computer Science vol. Volume 4828/2010, ed. Heidelberg: Springer Berlin, 2010, pp. 333-344.
  22. L. Bo, et al., "An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 37, pp. 18-27, 2007.
  23. L. Zhixiong and W. Shaomei, "Hybrid Particle Swarm Optimization for Permutation Flow Shop Scheduling," in Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on, 2006, pp. 3245-3249.
  24. L. Dasheng, et al., "A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 37, pp. 42-50, 2007.
  25. Y. G. Petalas, et al., "Enhanced Learning in Fuzzy Simulation Models Using Memetic Particle Swarm Optimization," in Swarm Intelligence Symposium, 2007. SIS 2007. IEEE, 2007, pp. 16-22.
  26. O. Schutze, et al., "A Memetic PSO Algorithm for Scalar Optimization Problems," in Swarm Intelligence Symposium, 2007. SIS 2007. IEEE, 2007, pp. 128-134.
  27. L. Hong-qi and L. Li, "A Novel Hybrid Particle Swarm Optimization Algorithm Combined with Harmony Search for High Dimensional Optimization Problems," in Intelligent Pervasive Computing, 2007. IPC. The 2007 International Conference on, 2007, pp. 94-97.
  28. M. R. AlRashidi and M. E. El-Hawary, "Hybrid Particle Swarm Optimization Approach for Solving the Discrete OPF Problem Considering the Valve Loading Effects," Power Systems, IEEE Transactions on, vol. 22, pp. 2030-2038, 2007.
  29. Z. Ruiyou and W. Dingwei, "Forecasting annual electricity demand using BP neural network based on three sub-swarms PSO," in Control and Decision Conference, 2008. CCDC 2008. Chinese, 2008, pp. 1409-1413.
  30. J. Zhang, et al., "Multi-sub-swarm particle swarm optimization algorithm for multimodal function optimization," in Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, 2007, pp. 3215-3220.
  31. L. Benameur, et al., "A New Hybrid Particle Swarm Optimization Algorithm for Handling Multiobjective Problem Using Fuzzy Clustering Technique," in Computational Intelligence, Modelling and Simulation, 2009. CSSim '09. International Conference on, 2009, pp. 48-53.
  32. S. Changyin, et al., "Clustering with a Weighted Sum Validity Function Using a Niching PSO Algorithm," in Networking, Sensing and Control, 2007 IEEE International Conference on, 2007, pp. 368-373.
  33. A. P. Engelbrecht and L. N. H. van Loggerenberg, "Enhancing the NichePSO," in Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, 2007, pp. 2297-2302.
  34. W. Junnian, et al., "Hill Valley Function Based Niching Particle Swarm Optimization for Multimodal Functions," in Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on, 2009, pp. 139-144.
  35. A. Nickabadi, et al., "DNPSO: A Dynamic Niching Particle Swarm Optimizer for multi-modal optimization," in Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on, 2008, pp. 26-32.
  36. R. Brits, et al., "Solving systems of unconstrained equations using particle swarm optimization," in Systems, Man and Cybernetics, 2002 IEEE International Conference on, 2002, p. 6 pp. vol.3.
  37. M. Qianzhi, et al., "Mobile Robot Path Planning with Complex Constraints Based on the Second-Order Oscillating Particle Swarm Optimization Algorithm," in Computer Science and Information Engineering, 2009 WRI World Congress on, 2009, pp. 244-248.
  38. M. R. AlRashidi and M. E. El-Hawary, "Emission-Economic Dispatch using a Novel Constraint Handling Particle Swarm Optimization Strategy," in Electrical and Computer Engineering, 2006. CCECE '06. Canadian Conference on, 2006, pp. 664-669.
  39. S. Li-quan and G. Xue-yao, "Improved Chaos-Particle Swarm Optimization Algorithm for Geometric Constraint Solving," in Computer Science and Software Engineering, 2008 International Conference on, 2008, pp. 992-995.
  40. C. Chun-Hong, et al., "The geometric constraint solving based on memory particle swarm algorithm," in Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on, 2004, pp. 2134-2139 vol.4.
  41. S. Sivasubramani and S. Swarup K, "Multiagent based particle swarm optimization approach to economic dispatch with security constraints," in Power Systems, 2009. ICPS '09. International Conference on, 2009, pp. 1-6.
  42. S. Chandrasekaran, et al., "Multi-objective particle swarm optimization algorithm for scheduling in flowshops to minimize makespan, total flowtime and completion time variance," in Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, 2007, pp. 4012-4018.
  43. H. Chen, et al., "RFID network planning using a multi-swarm optimizer," Journal of Network and Computer Applications, vol. In Press, Corrected Proof, 2010.
  44. M. A. Abido, "Multiobjective particle swarm optimization for environmental/economic dispatch problem," Electric Power Systems Research, vol. 79, pp. 1105-1113, 2009.
  45. A. C. Briza and P. C. Naval Jr, "Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data," Applied Soft Computing, vol. In Press, Corrected Proof, 2010.
  46. C. K. Goh, et al., "A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design," European Journal of Operational Research, vol. 202, pp. 42-54, 2010.
  47. B. Alatas and E. Akin, "Multi-objective rule mining using a chaotic particle swarm optimization algorithm," Knowledge-Based Systems, vol. 22, pp. 455-460, 2009.
  48. A. B. de Carvalho, et al., "A symbolic fault-prediction model based on multiobjective particle swarm optimization," Journal of Systems and Software, vol. 83, pp. 868-882, 2010.
  49. S. Dehuri and S. B. Cho, "Multi-criterion Pareto based particle swarm optimized polynomial neural network for classification: A review and state-of-the-art," Computer Science Review, vol. 3, pp. 19-40, 2009.
  50. J. Cai, et al., "A multi-objective chaotic particle swarm optimization for environmental/economic dispatch," Energy Conversion and Management, vol. 50, pp. 1318-1325, 2009.
  51. C.-t. Cheng, et al., "Comparison of particle swarm optimization and dynamic programming for large scale hydro unit load dispatch," Energy Conversion and Management, vol. 50, pp. 3007-3014, 2009.
  52. S. Z. Zhao, et al., "Dynamic multi-swarm particle swarm optimizer with local search for Large Scale Global Optimization," in Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on, 2008, pp. 3845-3852.
  53. Y. Wang, et al., "Trajectory planning for an unmanned ground vehicle group using augmented particle swarm optimization in a dynamic environment," San Antonio, TX, 2009, pp. 4341-4346.
  54. X. Liu, et al., "Particle swarm optimization based on dynamic niche technology with applications to conceptual design," Advances in Engineering Software, vol. 38, pp. 668-676, 2007.
  55. W. Du and B. Li, "Multi-strategy ensemble particle swarm optimization for dynamic optimization," Information Sciences, vol. 178, pp. 3096-3109, 2008.
  56. X. Yang, et al., "A modified particle swarm optimizer with dynamic adaptation," Applied Mathematics and Computation, vol. 189, pp. 1205-1213, 2007.
  57. C. Bae, et al., "Feature selection with Intelligent Dynamic Swarm and Rough Set," Expert Systems with Applications, 2010.
  58. C. Ying-Ping, et al., "Particle Swarm Optimization With Recombination and Dynamic Linkage Discovery," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 37, pp. 1460-1470, 2007.
  59. W. Zhenzhen and X. Hancheng, "Dynamic-probabilistic particle swarm synergetic model: A new framework for a more in-depth understanding of particle swarm algorithms," in Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on, 2008, pp. 312-321.
  60. J. Zhang, et al., "Particle swarm for the dynamic optimization of biochemical processes," in Computer Aided Chemical Engineering. vol. Volume 21, W. Marquardt and C. Pantelides, Eds., ed: Elsevier, 2006, pp. 497-502.
  61. W.-C. Yeh, "A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems," Expert Systems with Applications, vol. 36, pp. 9192-9200, 2009.
  62. P.-Y. Yin, "A discrete particle swarm algorithm for optimal polygonal approximation of digital curves," Journal of Visual Communication and Image Representation, vol. 15, pp. 241-260, 2004.
  63. A. Unler and A. Murat, "A discrete particle swarm optimization method for feature selection in binary classification problems," European Journal of Operational Research, vol. 206, pp. 528-539, 2010.
  64. W.-C. Yeh, et al., "A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method," Expert Systems with Applications, vol. 36, pp. 8204-8211, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Particle Swarm Optimization (PSO) Variant PSO Modification PSO Basic PSO problem Bird Flocking Evolutionary Optimization biologically inspired computational search

Powered by PhDFocusTM