CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-11-23
Cited: 1
Clicked: 9488
Hao-wei Zhang, Jun-wei Xie, Wen-long Lu, Chuan Sheng, Bin-feng Zong. A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(11): 1806-1816.
@article{title="A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar",
author="Hao-wei Zhang, Jun-wei Xie, Wen-long Lu, Chuan Sheng, Bin-feng Zong",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="11",
pages="1806-1816",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601358"
}
%0 Journal Article
%T A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar
%A Hao-wei Zhang
%A Jun-wei Xie
%A Wen-long Lu
%A Chuan Sheng
%A Bin-feng Zong
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 11
%P 1806-1816
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601358
TY - JOUR
T1 - A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar
A1 - Hao-wei Zhang
A1 - Jun-wei Xie
A1 - Wen-long Lu
A1 - Chuan Sheng
A1 - Bin-feng Zong
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 11
SP - 1806
EP - 1816
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601358
Abstract: A hybrid optimization approach combining a particle swarm algorithm, a genetic algorithm, and a heuristic interleaving algorithm is proposed for scheduling tasks in the multifunction phased array radar. By optimizing parameters using chaos theory, designing the dynamic inertia weight for the particle swarm algorithm as well as introducing crossover operation and mutation operation of the genetic algorithm, both the efficiency and exploration ability of the hybrid algorithm are improved. Under the frame of the intelligence algorithm, the heuristic interleaving scheduling algorithm is presented to further use the time resource of the task waiting duration. A large-scale simulation demonstrates that the proposed algorithm is more robust and efficient than existing algorithms.
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