Maximum Power Point Tracking for Photovoltaic System Based on IMVO Algorithm

Wu, Zheng-Mao; Cao, Bilian; Hou, Lincheng; Hu, Xiaobin; Ma, Boyan

doi:10.1007/s42835-022-01038-x

Cited by 3 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, in the MOMVO algorithm,

{W}_{ep}

controls the selective renewal or exchange of the

j

th‐dimensional components of the offspring universe, and its value increases linearly with the number of iterations. The traditional

{W}_{ep}

expression is unfavorable to full exchange of position information among the universe individuals in the early stage of MOMVO, and the significance of the

j\mathrm{th}

dimension exchange in the universe individuals will lost due to the population assimilation in the later stage 60 . Therefore, in order to promote the interaction of location information between individuals in the universe and improve the iterative optimization performance of the MOMVO algorithm, a suitable

{W}_{ep}

expression is proposed.…”

Section: Algorithmmentioning

confidence: 99%

“…The traditional W ep expression is unfavorable to full exchange of position information among the universe individuals in the early stage of MOMVO, and the significance of the jth dimension exchange in the universe individuals will lost due to the population assimilation in the later stage. 60 Therefore, in order to promote the interaction of location information between individuals in the universe and improve the iterative optimization performance of the MOMVO algorithm, a suitable W ep expression is proposed. Referring to the progressive idea of ISDT, the modified W ep can be expressed as follows.…”

Section: Renewal Strategy For Inter-generational Individualsmentioning

confidence: 99%

See 1 more Smart Citation

A multi‐objective optimization approach for beam pattern synthesis of UAV virtual rectangular antenna array

Mei,

Guo,

Kang

et al. 2024

Int J Numerical Modelling

View full text Add to dashboard Cite

Virtual antenna array (VAA) formed by unmanned aerial vehicle (UAV) antenna units using collaborative beamforming (CB) technology plays an important role in the air communication system, and can be used in radar, military, disaster rescue and other places. However, there are still some issues with the beam pattern formed by this method, such as high sidelobe level (SLL), high cost and low efficiency. In this article, each UAV carries an omnidirectional antenna unit, and a large number of UAVs form a UAV virtual rectangular antenna array (UVRAA) to communicate with the ground base station (BS). We formulate an overhead minimization and efficient communication multi‐objective optimization problem (OMECMOP) which jointly optimize the excitation current weights of the UVRAA and reduce the number of UAVs in operation to improve the beam pattern, enhance the communication efficiency and decrease the overhead of UVRAA. In addition, we also propose an improved multi‐objective multi‐verse optimization algorithm based on the inverse decline curve type (ISDT‐MOMVO) which introduces a strategy optimization initialization solution with quasi‐opposition based learning (QBL) and a hybrid solution updating operators to solve the OMECMOP. The simulation results show that compared with other traditional swarm intelligence (SI) optimization algorithms the ISDT‐MOMVO algorithm produces better beam pattern and the thinning rate can reach 50%.

show abstract

“…In addition, in the MOMVO algorithm,

{W}_{ep}

controls the selective renewal or exchange of the

j

th‐dimensional components of the offspring universe, and its value increases linearly with the number of iterations. The traditional

{W}_{ep}

expression is unfavorable to full exchange of position information among the universe individuals in the early stage of MOMVO, and the significance of the