TY - JOUR
T1 - Novel particle swarm optimization algorithm based on president election
T2 - Applied to a renewable hybrid power system controller
AU - Yahyazadeh, M.
AU - Johari, M. S.
AU - HosseinNia, S. H.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Chaotic Function
KW - Hybrid Optimization Algorithm
KW - Hybrid Power System
KW - Particle Swarm Optimization Algorithm
KW - President Election Algorithm
UR - http://www.scopus.com/inward/record.url?scp=85100300050&partnerID=8YFLogxK
U2 - 10.5829/IJE.2021.34.01A.12
DO - 10.5829/IJE.2021.34.01A.12
M3 - Article
AN - SCOPUS:85100300050
SN - 1728-1431
VL - 34
SP - 97
EP - 109
JO - International Journal of Engineering, Transactions A: Basics
JF - International Journal of Engineering, Transactions A: Basics
IS - 1
ER -