Novel particle swarm optimization algorithm based on president election: Applied to a renewable hybrid power system controller

被引：0

作者：

Yahyazadeh M. ^{[1
]}

Johari M.S. ^{[1
]}

HosseinNia S.H. ^{[2
]}

机构：

[1] Faculty of Engineering, Vali-e-Asr University of Rafsanjan

[2] Faculty 3mE, Departmant Precision and Microsystems Engineering, Mekelweg 2, DELFT

来源：

International Journal of Engineering, Transactions A: Basics | 2021年 / 34卷 / 01期

关键词：

Chaotic Function; Hybrid Optimization Algorithm; Hybrid Power System; Particle Swarm Optimization Algorithm; President Election Algorithm;

D O I：

10.5829/IJE.2021.34.01A.12

中图分类号：

学科分类号：

摘要：

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. © 2021 Materials and Energy Research Center. All rights reserved.

引用

页码：97 / 109

页数：12

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