Novel Particle Swarm Optimization Algorithm Based on President Election: Applied to a Renewable Hybrid Power System Controller

Document Type : Original Article


1 Faculty of Engineering, Vali-e-Asr University of Rafsanjan, Iran

2 Department of Precision and Microsystems Engineering, Mekelweg 2, 2628 CD DELFT, The Netherlands


Particle swarm optimization has been a popular and common met heuristic algorithm from its genesis time. However, some problems such as premature convergence, weak exploration ability and great number of iterations have been accompanied with the nature of this algorithm. Therefore, in this paper we proposed a novel classification for particles to organize them in a different way. This new method which is inspired from president election is called President Election Particle Swarm Optimization (PEPSO). This algorithm is trying to choose useful particles and omit functionless ones at initial steps of algorithm besides considering the effects of all generated particles to get a directed and fast convergence. Some preparations are also done to escape from premature convergence. To validate the applicability of our proposed PEPSO, it is compared with the other met heuristic algorithm including GAPSO, Logistic PSO, Tent PSO, and PSO to estimate the parameters of the controller for a hybrid power system. Results verify that PEPSO has a better reaction in worst conditions in finding parameters of the controller.


Gao, H., Kwong, S., Yang, J., and Cao, J., “Particle swarm optimization based on intermediate disturbance strategy algorithm and its application in multi-threshold image segmentation”, Information Sciences, Vol. 250, (2013), 82-112. DOI: 10.1016/j.ins.2016.07.017.
Soufi, Y., Bechouat, M., and Kahla, S., “Fuzzy-PSO controller design for maximum powerpoint tracking in photovoltaic system”, International Journal of Hydrogen Energy, Vol. 42, No. 13, (2017), 8680-8688. DOI: 10.1016/j.ijhydene.2016.07.212.
Khanna, V., Das, B.K., Bisht, D., Vandana, and Singh, P.K., “A three diode model for industrial solar cells and estimation of solar cell parameters using PSO algorithm”, Renewable Energy, Vol. 78, (2019), 105-113. DOI: 10.1016/j.renene.2014.12.072.
Yahyazadeh, M. and Rezaeeye, H., “Optimal Placement and Sizing of Distributed Generation Using Wale Optimization Algorithm Considering Voltage Stability and Voltage Profile Improvement, Power Loss and Investment Cost Reducing”, Iranian Journal of Science and Technology, Transactions of Electrical Engineering, Vol. 44, (2020), 227-236. DOI: 10.1007/s40998-019-00224-4.
Gholami Dehbalaeea, M.R., Shaeisia, G.H., and Valizadeh M., “A Proposed Improved Hybrid Hill Climbing Algorithm with the Capability of Local Search for Solving the Nonlinear Economic Load Dispatch Problem”, International Journal of Engineering, Transactions A: Basics, Vol. 33, No. 4, (2020), 575-585. DOI: 10.5829/ije.2020.33.04a.09.
Park, J-B., Jeong, Y-W., Shin, J-R., and Lee, K.Y., “An Improved Particle Swarm Optimization for Nonconvex Economic Dispatch Problems”, IEEE Transactions on Power Systems, Vol. 25, NO., (2010), 156-166. DOI: 10.1109/TPWRS.2009.2030293.
Madoliat, R., Khanmirza, E., and Pourfard, A., “Application of PSO and Cultural Algorithms forTransient Analysis of Natural Gas Pipeline”, Journal of Petroleum Science and Engineering, Vol. 149, (2017), 504-514. DOI: 10.31590/ejosat.717872.
Akbarpour H., Karimi G., and Sadeghzadeh A., “Discrete Multi Objective Particle Swarm Optimization Algorithm for FPGA Placement (Research Note)”, International Journal of Engineering, Transactions C: Aspects, Vol. 28, No. 3, (2015), 410-418. DOI: 10.5829/idosi.ije.2015.28.03c.10.
Jam S., Shahbahrami A., and Ziyabari, S.H.S., “Parallel Implementation of Particle Swarm Optimization Variants Using Graphics Processing Unit Platform”, International Journal of Engineering, Transactions A: Basics, Vol. 30, No. 1, (2017), 48-56. DOI: 10.5829/idosi.ije.2017.30.01a.07.
Sancaktar I., Tuna B., and Ulutas M., “Inverse kinematics application on medical robot using adapted PSO method”, Engineering Science and Technology, an International Journal, Vol. 21, No. 5, (2018), 1006-1010. DOI: 10.1016/j.jestch.2018.06.011.
Abdelshafy, A.M., Hassan, H., and Jurasz, J., “Optimal design of a grid-connected desalination plant powered by renewable energy resources using a hybrid PSO–GWO approach”, Energy Conversion and Management, Vol. 173, (2018), 331-347. DOI: 10.1016/j.enconman.2018.07.083.
Jana, B., Mitra, S., and Acharyaa, S., “Repository and Mutation based Particle Swarm Optimization (RMPSO): A new PSO variant applied to reconstruction of gene regulatory network”, Applied Soft Computing, Vol. 74, (2019), 330-355. DOI: 10.1016/j.asoc.2018.09.027.
Anescu, G. and Paul Ulmeanu, A., “A No Speeds and Coefficients PSO approach to reliability optimization problems”, Computers & Industrial Engineering, Vol. 120, (2018), 31-41. DOI: 10.1016/j.cie.2018.04.020.
Liu P. and Liu J., “Multi-leader PSO (MLPSO): A new PSO variant for solving global optimization problems”, Applied Soft Computing, Vol. 61, (2017), 256-263. DOI: 10.1016/j.asoc.2017.08.022.
Alatas B., Akin E., and Bedri Ozer A., “Chaos Embedded Particle Swarm Optimization Algorithms”, Chaos Solitons and fractals, Vol. 40, No. 4, (2009), 1715-1734. DOI: 10.1016/j.chaos.2007.09.063.
Pan I. and Das S., “Fractional Order Fuzzy Control of Hybrid Power System with Renewable Generation Using Chaotic PSO”, ISA Transaction, Vol. 62, (2016), 19-29. DOI: 10.1016/j.isatra.2015.03.003.
Monje, C.A., Chen, Y.Q.,  Vinagre, B.M., Xue, D., and  Feliu, V., Fractional-order systems and controls: fundamental and applications, Advanced Industrial Control Series, Springer-Verlag, (2010).
Das, S., Pan, I., and Das, Sh., “Performance Comparison of Optimal Fractional order Hybrid Fuzzy PID Controllers For Handling Oscillatory Fractional Order Processes with Dead Time”, ISA Transactions, Vol. 52, No. 4, (2013), 550-566. DOI: 10.1016/j.isatra.2013.03.004.
Zhang, Q., Ogren, R.M., and Kong, S., “A comparative study of biodiesel engine performance optimization using enhanced hybrid PSO–GA and basic GA”, Applied Energy, Vol. 165, (2016), 676-684. DOI: 10.1016/j.apenergy.2015.12.044.