MSSA: A M-Level Sufferage-Based Scheduling Algorithm in Grid Environment

Panda, Sanjaya Kumar; Khilar, Pabitra Mohan

doi:10.1007/978-3-642-36071-8_32

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…For comparisons, we select several benchmark SI optimization algorithms to demonstrate the effectiveness of the ISDT‐MOMVO algorithm in solving OMECMOP 64 . Confirmed by simulation, the third non‐dominated sorting genetic algorithm (NSGA‐III), 63 conventional multi‐objective particle swarm algorithm (MOPSO), 27 MOALO, 35 MSSA 65,66 have high convergence and the iterative solutions approximate the Pareto optimal solution, which has significant advantages in solving challenging practical engineering problems and is suitable for solving nonlinear NP‐hard problems. The fixed parameter values of the above algorithms are shown in Table 1.…”

Section: Simulationsmentioning

confidence: 99%

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

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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%.

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