An Expert System for Intelligent Selection of Proper Particle Swarm Optimization Variants

Document Type : Research Paper


1 Tarbiat Modares University, Teahran, Iran

2 Middle East Technical University, Ankara, Turkey

3 Islamic Azad University, Saveh branch, saveh, Iran


Regarding the large number of developed Particle Swarm Optimization (PSO) algorithms and the various applications for which PSO has been used, selecting the most suitable variant of PSO for solving a particular optimization problem is a challenge for most researchers. In this paper, using a comprehensive survey and taxonomy on different types of PSO, an Expert System (ES) is designed to identify the most proper PSO for solving different optimization problems. Algorithms are classified according to aspects like particle, variable, process, and swarm. After integrating different acquirable information and forming the knowledge base of the ES consisting 100 rules, the system is able to logically evaluate all the algorithms and report the most appropriate PSO-based approach based on interactions with users, referral to knowledge base and necessary inferences via user interface. In order to examine the validity and efficiency of the system, a comparison is made between the system outputs against the algorithms proposed by newly published articles. The result of this comparison showed that the proposed ES can be considered as a proper tool for finding an appropriate PSO variant that matches the application under consideration.


Main Subjects

Aguirre A. H., Muñoz Zavala A. E. and Diharce E. V., and Botello Rionda S. (2007). COPSO: Constraints Optimization via PSO algorithm. Comunicación Técnic, (CC/CIMAT).
Altinoz O. T., Yilmaz A. E, Weber G. W. (2012). Application of chaos embedded PSO for PID parameter tuning. International J. of Computers, Communications and Control vol. 7, no. 2, pp. 204-217.
Alviar J.-B., Peña J., and Hincapié R., (2007). Subpopulation best rotation: a modification on PSO. Revista Facultad de Ingeniería. Vol. 40, pp.118-122.
Atyabi A. and Phon-Amnuaisuk S. (2007). Particle swarm optimization with area extension (AEPSO). IEEE/CEC, pp. 1970-1976.
Behera H. S., Dash P. K. and Biswal B. (2010). Power quality time series data mining using stransform and fuzzy expert system. Appl. Soft Comput. J. Vol. 10, No. 3, pp. 945–955.
Brits R., Engelbrecht A. P., and van den Bergh F. (2002). Solving Systems of unconstrained equations using particle swarm optimization. Proc. of IEEE Conf. on Sys. Man and Cyber. (SMC), pp. 102-107.
Brits R., Engelbrecht A.P., and van den Bergh F., (2005). Niche Particle Swarm Optimization. Technical report, Department of Computer Science, University of Pretoria.
Chandrasekaran S., Ponnambalam S.G., Suresh R.K. and Vijayakumar N. (2006). A Hybrid Discrete Particle Swarm Optimization Algorithm to Solve Flow Shop Scheduling Problems. Proc. IEEE/ ICCIS, pp.1–6.
Chen C. Y., Feng H. M., and Ye F. (2007). Hybrid Recursive Particle Swarm Optimization Learning Algorithm in the Design of Radial Basis Function Networks. J. of Marine Science and Technology, Vol. 15, No. 1, pp. 31-40.
Cui Z., Zeng J. And Cai X. (2004). A guaranteed convergence dynamic double particle swarm optimizer. Fifth World Cong. on Intell., Control and Automation, Vol. 3, pp. 2184 – 2188. 
Felix T. S. Chan1, Kumar, V. and Mishra, N. (2007). A CMPSO algorithm based approach to solve the multi-plant supply chain problem. Swarm Intell.: Focus on Ant and Particle Swarm Optimization, pp. 532.
Feng H.-M. (2005). Self-generation fuzzy modeling systems through hierarchical recursive-based particle swarm optimization. J. of Cyber. and Systems, Vol. 36, No. 6, pp. 623-639.
Galvez A. and Iglesias A. (2013). A new iterative mutually-coupled hybrid GA-PSO approach for curve fitting in manufacturing. Applied Soft Computing, No. 13, Vol. 3, pp. 1491–1504.
Ghamisi P., Couceiro M.S., Martins F.M.L., and Benediktsson J.A. (2013). Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization. J. of Geoscience and Remote Sensing, Vol. pp, Issue. 99, pp. 1-13, June 2013.
Gheitanchi S., Ali F. H. and Stipidis E. (2008). Trained Particle Swarm Optimization for Ad-hoc Collaborative Computing Networks. Swarm Intell. Algorithms and Applications Symp., ASIB, UK.
Giarratano J. C. and Riley G. (1998). Expert Systems: Principles and Programming. PWS Publishing Company.
He S., Wu Q.H., Wen J.Y., Saunders J.R. and Paton R.C. (2004). A particle swarm optimizer with passive congregation. J. of Biosystems, Vol.78, No.1-3, pp. 135-147.
Higashitani M., Ishigame A. and Yasuda K. (2008). Pursuit-Escape Particle Swarm Optimization. Trans. On Electrical and Electronic Eng., (IEEJ), Vol.3, No.1, pp.136–142.
Ho S.-Y., Lin H.-S., Liauh W.-H., and Ho S.-J. (2008). OPSO: Orthogonal Particle Swarm Optimization and Its Application to Task Assignment Problems. IEEE Trans. on Systems, Man and Cyber., Part A, Vol. 38, No., 2, pp. 288-298.
Hu X. and Eberhart R. C. (2002). Multi objective optimization using dynamic neighborhood particle swarm optimization. Proc. of the IEEE/ CEC, pp. 1677-1681.
Hui W. and Feng Q. (2007). Improved particle swarm optimizer with behavior of distance models. J. of Computer Eng. and Applications, 43 (30): 30-32.
Jang W.-S., Kang H.-I., Lee B.-H., Kim K.-I., Shin D.-I. and Kim S.-C. (2007). Optimized fuzzy clustering by predator prey particle swarm optimization. In IEEE/CEC, pp. 3232-3238.
Jarbouia B., Cheikha M., Siarryb P. and Rebaic A., (2007). Combinatorial particle swarm optimization (CPSO) for partitioned clustering problem. J. Applied Mathematics and Computation, Vol. 192, Issue 2, 15 pp. 337-345.
Ji C., Zhang Y., Gao SH., Yuan P. and Li Zh. (2004). Particle swarm optimization for mobile ad hoc networks clustering. IEEE Int. Conf. on Networking, Sensing and Control, Vol. 1, pp. 372–375.
Jie J., Zeng j. and Han C. (2006). Self-Organization Particle Swarm Optimization Based on Information Feedback. Advances in natural comput.: ( Part I-II: Second Int. conf., ICNC 2006, Xi'an, China. 
Jingbo A. and Hongfei T., (2005). Cultural based Particle Swarm Optimization. Center for Science and Technology Development, Ministry of Education P. R. China.
Keedwell E., Morley M., and Croft D. (2012). Continuous Trait-Based Particle Swarm Optimisation (CTB-PSO). 8th International Conference, Brussels, Belgium, Vol. 7461, pp 342343 Sept.
Kennedy J. and Eberhart R. C. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, Vol. IV, pp. 1942-1948.
Kennedy J. and Eberhart R. C. (2001). Swarm Intelligence. Morgan Kaufmann, San Francisco, CA.
Kennedy J. and Eberhart R.C. (1997). A discrete binary version of the particle swarm algorithm. Int. IEEE Conf. on Systems, Man, and Cyber. Vol.5, pp.4104–4108.
Koh B-Il, Fregly B.-J., George A.-D. and Haftka R.-T. (2005). Parallel asynchronous particles swarm for global biomechanical. Int J Number Methods Eng., Vol. 67(4), pp. 578–595.
Krink T. Vesterstrom J.S. and Riget J. (2002). Particle swarm optimization with spatial particle extension. Proc. of Cong. on Evolutionary Computation, (CEC’02), Vol. 2, pp. 1474-1479.
Kulkarni R.V. and Venayagamoorthy G.K. (2007). An Estimation of Distribution Improved Particle Swarm Optimization Algorithm. Proc. IEEE/ ISSNIP, pp. 539-544.
Lam H. T., Nikolaevna P. N., and Quan N. T. M. (2007). The Heuristic Particle Swarm Optimization. Proc. of annual Conf. on Genetic and evolutionary computation in Ant colony optimization, swarm Intell. and artificial immune systems,”GECCO’07”, pp.174–174.
Lee C. H., Lee Y. C., and Chang F. Y. (2010). A Dynamic Fuzzy Neural System Design via Hybridization of EM and PSO Algorithms. IAENG International J. of Computer Science, 37:3.
Lee K. H., Baek S. W. and Kim K. W. (2008). Inverse radiation analysis using repulsive particle swarm optimization algorithm. International Journal of Heat and Mass Transfer; Vol. 51(11-12), pp. 2772– 2783.
Lee, T. Y. (2007). Optimal Spinning Reserve for a Wind-Thermal Power System Using EIPSO. IEEE/ TPWRS, Vol. 22(4), pp. 1612–1621.
Li H. Q. and Li L. (2007). A novel hybrid particle swarm optimization algorithm combined with harmony search for high dimensional optimization problems. International Conference on Intelligent Pervasive Computing, Jeju Island, Korea.
Li H.-Q. and Li L. (2007). A Novel Hybrid Particle Swarm Optimization Algorithm Combined with Harmony Search for High Dimensional Optimization Problems. Proc. IEEE/IPC, pp. 94-97.
Li T., Lai X. and Wu M. (2006). An Improved Two-Swarm Based Particle Swarm Optimization Algorithm. Proc. IEEE/ WCICA, Vol. 1, pp. 3129-3133.
Li X. (2004). Adaptively Choosing Neighborhood Bests using Species in a Particle Swarm Optimizer for Multimodal Function Optimization. Proc. of GECCO LNCS 3102, pp.105-116.
Liang J. J., Qin A. K. and Baskar S. (2006). Comprehensive Learning Particle Swarm Optimizer for Global Optimization of multimodal Functions. IEEE Trans. Evolutionary Computation, Vol. 10, No. 3.
Liao c.y., Lee W. P. and Chen X. (2007). Dynamic and adjustable particle swarm optimization, Proc. of the 8th Conf. on 8th WSEAS Int. Conf. on Evolutionary Computing, Vol. 8.
Lin C., Liu Y. and Lee C. (2008). An efficient neural fuzzy network based on immune particle swarm optimization for prediction and control applications. J. of Innovative Computing, Information and Control, Vol. 4(7), pp.1711-1722.
Lu H. and Chen W. (2006). Dynamic-objective particle swarm optimization for constrained optimization problems. J. of Combinatorial Optimization, Vol. 12(4), PP. 409-419.
Lu H. and Chen W. (2008). Self-adaptive velocity particle swarm optimization for solving constrained optimization problems. J. of Global Optimization, Vol.41(3), pp. 427-445.
M. Neethling, and A. Engelbrecht. (2006). Determining RNA Secondary Structure using Setbased Particle Swarm Optimization. In Proc. IEEE Cong. on Evolutionary Computation, Vancouver, pp. 1670-1677.
Madar J., Abonyi J. and Szeifert F. (2005). Interactive particle swarm optimization. Proc. Int. IEEE conf. On Intelligent Systems Design and Applications (IEEE/ ISDA), Vol.8(10), pp.314 – 319.
McNabb A. W., Monson C. K. and Seppi K. D. (2007). MRPSO: Map Reduce particle swarm optimization. Proc. of the 9th annual Conf. on Genetic and evolutionary Comput., pp. 177–177.
Medeiros D. J., Swenson E. and DeFlitch C. (2008). Improving Patient Flow in a Hospital Emergency Department. Proc. Winter Simulation Conf. Austin, TX, pp.1526-1531.
Meissner M., Schmuker M. and Schneider G. (2006). Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training. BMC Bioinformatics, Vol. 7, pp. 125.
Miranda V. and Fonseca N. EPSO – Best-of-two-worlds Meta-heuristic Applied to Power System problems. In Proc. of the IEEE Congress on Evolutionary Computation, Honolulu, Vol. 2, pp. 1080-1085.
Mo Y., Chen D. and Hu S. (2006). Chaos particle swarm optimization algorithm and its application in biochemical process dynamic optimization, J. Chem. Ind. Eng. (China), Vol. 57(9), pp. 2123–2127.
Monson C. K. and Seppi K. D. (2005). Linear Equality Constraints and Homomorphous Mappings in PSO. IEEE Congress on Evolutionary Computation (CEC’2005), Vol. 1, IEEE Service Center, Edinburgh, Scotland, pp. 73–80.
Moore P.W. and Venayagamoorthy G.K. (2006). Empirical Study of an Unconstrained Modified Particle Swarm Optimization. IEEE/CEC, pp. 1477-1482.
Moraglio A., Di Chio C., Togelius J. and Poli R. (2008). Geometric Particle Swarm Optimization. J. of Artificial Evolution and Applications.
Moraglio A., Di Chio C., Togelius J. and Poli, R. (2008). Geometric Particle Swarm Optimization. J. of Artificial Evolution and Applications.
Neethling M. and Engelbrecht A. P. (2006). Determining rna secondary structure using set-based particle swarm optimization. In IEEE Congress on Evolutionary Computation. CEC 2006., pages 1670–1677.
Noel M.M. and Jannett T.C. (2004). Simulation of a new hybrid particle swarm optimization algorithm. Proc., Of the IEEE Symp. On System Theory, pp. 150–153.
Omkar S. N., Mudigere D., Narayana Naik G., and Gopalakrishnan S. (2008). Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures. J. Computers and Structures, Vol. 86, No(1-2), pp .1-14.
Özcan E. and Yılmaz M. (2006). Particle Swarms for Multimodal Optimization. Lecture Notes In Computer Science; Vol. 4431, Proc. of the 8th int. conf. on Adaptive and Natural Computing Algorithms, Part I. pp. 366–375.
Pampara G., Franken N. and Engelbrecht A.P. (2005). Combining particle swarm optimization with angle modulation to solve binary problems. The IEEE Cong. on Evolutionary Comput., Vol. 1, pp. 89-96.
Pant M., Radha T. and Singh V.P. (2007). A New Particle Swarm Optimization with Quadratic Interpolation. Int. IEEE Conf. on Computational Intell. and Multimedia Applications, Vol.1, pp. 55-60.
Pant, M., Thangaraj, R. and Abraham, A. (2008). Particle Swarm Optimization Using Adaptive Mutation. IEEE/DEXA'08, pp. 519-523.
Parsopoulos K. E. and Vrahatis M. N. (2004). UPSO: A Unified Particle Swarm Optimization Scheme. Proc. of the Int. Conf. of Computational Methods in Sci. and Eng., Vol. 1, pp. 868-873.
Pasupuleti S. and Battiti R. (2006). The Gregarious Particle Swarm Optimizer (GPSO). GECCO’06.
Poli R., Langdon W. B., and Holland O. (2005). Extending particle swarm optimization via genetic programming. In M.
Keijzer et al. (Eds.), Lecture notes in computer science: Vol. 3447. Proc. of the 8th European conf. on genetic programming, pp. 291–300, Springer.
Rezaee Jordehi A., Jasni J., Abd Wahab N., Kadir M.Z., Javadi M.S. (2015). Enhanced leader PSO (ELPSO): a new algorithm for allocating distributed TCSC’s in power systems. Int. J. Electr. Power Energy Syst, Vol. 64, pp. 771-784.
Riget J. and Vesterstroem J. S. (2002). A diversity-guided particle swarms optimizer - the ARPSO. Technical Report No. 2002-02. Dept. of Computer Science, University of Aarhus, EVALife.
Roy R. and Ghoshal S.P. (2006). Evolutionary computation based optimization in fuzzy 
automatic generation control. IEEE/ POWERI, pp.7.

Sadri J. and Suen C.Y. (2006). A Genetic Binary Particle Swarm Optimization Model. Proc. IEEE/CEC, pp.656 – 663.
Schoeman I. L. and Engelbrecht A. P. (2004). Using Vector Operations to Identify Niches for Particle Swarm Optimization. In Proc. of the IEEE Conf. on Cyber. and Intelligent Sys. PP. 361366.
Schoeman I. L. and Engelbrecht A. P. (2004). Using vector operations to identify niches for particle swarm optimization. in Proceedings of IEEE Conference on Cybernetics and Intelligent Systems (CCIS), Vol. 1, pp. 361–366, Singapore.
Secrest B.R. and Lamont G.B. (2003). Visualizing particle swarm optimization – Gaussian particle swarm optimization. Proc. Of Swarm Intell. Symp. (IEEE/SIS), pp. 198-204.
Sedighizadeh D. and Masehian E. (2009a). A New Taxonomy for Particle Swarm Optimization (PSO). Proc. 10th Int. Conf. on Automation Technology, pp. 317-322.
Sedighizadeh D. and Masehian E. (2009b). Particle Swarm Optimization Methods, Taxonomy and Applications. International Journal of Computer Theory and Engineering, Vol.1, pp.486-502.
Sedlaczek K. and Eberhard P. (2006). Using Augmented Lagrangian Particle Swarm Optimization for Constrained Problems in Engineering. J. of Structural and Multidisciplinary Optimization, Vol. 32, No. 4, pp. 277-286.
Shen X., Wei K., Wu D., Tong Y. and Li Y. (2007). A Dynamic Adaptive Dissipative Particle Swarm Optimization with Mutation Operation. Proc. IEEE/ ICCA, pp. 586-589.
Shi Y. and Eberhart R. (2001). Fuzzy Adaptive Particle Swarm Optimization. Proc. IEEE / Cong. on Evolutionary Computation. Seoul, vol. 1, pp. 101-106.
Shi Y., and Krohling R. A. (2002). Co-evolutionary particle swarm optimization to solve minmax problems. in Proc. Cong. on Evolutionary Comput., Vol. 2, pp. 1682-1687.
Subrarnanyam V. Srinivasan D. and Oniganti R. (2007). Dual layered PSO Algorithm for evolving an Artificial Neural Network controller. IEEE/CEC, pp. 2350-2357.
Sun J., Feng B. and Xu W. (2004). Particle swarm optimization with particles having quantum behavior. Proc. IEEE/CEC, Vol. 1, pp. 325–331.
Tao Q., Chang H. Y., Yi Y., Gu C. Q., and Yu Y. Qo S. (2009). constrained grid workflow scheduling optimization based on a novel PSO algorithm. in 8th International Conference on Grid and Cooperative Computing, Guangzhou, China, pp. 153-159.
Voss M.S. (2005). Principal component particle swarm optimization (PCPSO). Proc., Of the IEEE Symp. On swarm Intell., pp. 401–404.
Wang H., and Qian F. (2007). An improved particle swarm optimizer with shuffled sub-swarms 
and its application in soft-sensor of gasoline endpoint. Proc. Advances in Intelligent Systems Research.

Wang H., and Qian F. (2007). An improved particle swarm optimizer with shuffled sub-swarms and its application in soft-sensor of gasoline endpoint. Proc. Advances in Intelligent Systems Research.
Wang X. H. and Li J.-J. (2004). Hybrid particle swarm optimization with simulated annealing. Proc., Of the IEEE Int. Conf. on Machine Learning and Cyber., Vol. 4, pp. 2402–2405.
Waterman D. A. (1986). A guide to expert systems, illustrated, reprint ed. California, USA: Addison-Wesley.
Wei C., He Z., Zhang Y. and Pei W. (2002). Swarm directions embedded in fast evolutionary programming . In Proc. of the IEEE/CEC, pp. 1278–1283.
Wei K., Zhang T., Shen X. and Liu J. (2007). An Improved Threshold Selection Algorithm Based on Particle Swarm Optimization for Image Segmentation. Proc. IEEE/ICNC.
Xiao-ping X., QIAN F. C. and Feng W. (2008). Research on new method of system identification based on velocity mutation Particle Swarm Optimization. (In Chinese), 44 (1) pp. 31-34.
Xie X.-F., Zhang W.-J. and Yang Z.-L. (2002a). Adaptive Particle Swarm Optimization on Individual Level. Int. Conf. On Signal Processing (ICSP), pp. 1215-1218.
Xie X.-F., Zhang W.-J., and Yang Z.-L. (2002b). A Dissipative Particle Swarm Optimization. Cong. on Evolutionary Comput. (CEC), pp. 1456-1461.
Xu F. and Chen W. (2006). Stochastic Portfolio Selection Based on Velocity Limited Particle Swarm Optimization. Proc. IEEE/ WCICA, Vol. 1, pp. 3599-3603.
Yang C. and Simon D. (2005). A New Particle Swarm Optimization Technique. Proc. Of the Int. Conf. on systems Eng., (IEEE/ISEng’05).
Yang S., Wang M. and Jiao L. (2004). A Quantum Particle Swarm Optimization. In Proceedings of Congress on Evolutionary Computation CEC2004, vol.1. pp. 320-324.
Yang W.-P. (2007). Vertical Particle Swarm Optimization Algorithm and its Application in SoftSensor Modeling. Int. Conf. on Machine Learning and Cyber. (IEEE/ ICMLC), Vol.4, pp. 1985-1988.
Yao X. (2008). Cooperatively Coevolving Particle Swarms for Large Scale Optimization. Conf. of EPSRC, Artificial Intell. Technologies New and Emerging Computer Paradigms.
Yin P. Y. (2006). Genetic particle swarm optimization for polygonal approximation of digital curves. J. of Pattern Recognition and Image Analysis. Vol. 16(2), pp. 223-233.
Yuan L. and Zhao Z.-D. (2007). A Modified Binary Particle Swarm Optimization Algorithm for Permutation Flow Shop Problem. IEEE/ICMLC, Vol.2, pp. 902-907.
Yuan Z., Jin R., Geng J., Fan Y., Lao J., Li J., Rui X., Fang Z. and Sun J. (2005). A perturbation particle swarm optimization for the synthesis of the radiation pattern of antenna array. Proc. IEEE Conf. on Asia-Pacific, Vol.3, No, 4-7.
Zeng J., Hu J. and Jie J. (2006). Adaptive Particle Swarm Optimization Guided by Acceleration Information. Proc. IEEE/ ICCIAS, Vol.1, pp. 351-355.
Zhang Q. and Mahfouf M. (2006). A New Structure for Particle Swarm Optimization (nPSO) Applicable to Single Objective and Multi objective Problems. Int. IEEE Conf. on Intelligent Systems, pp.176–181.
Zhang X., Hu W., Maybank S., Li X. and Zhu M. (2008). Sequential particle swarm optimization for visual tracking. IEEE/CVPR, pp. 1-8.
Zhang Y.-N., Hu Q.-N. and Teng H.-F. (2008). Active target particle swarm optimization: Research Articles. J. of Concurrency and Computation: Practice & Experience, Vol.20, No.1, pp. 29–40.
Zhao B. (2006). An Improved Particle Swarm Optimization Algorithm for Global Numerical Optimization. Int. Conf. on Comput. Science N6, Reading, (Royaume-uni), Vol. 3994, pp. 657-664.
Zhiming L., Cheng W. and Jian L. (2008). Solving constrained optimization via a modified genetic particle swarm optimization. Proc. of Int. Conf. On Forensic applications and techniques in telecommunications, information, and multimedia and workshop, No. 49.