A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar

Hao-wei ZHANG; Jun-wei XIE; Wen-long LU; Chuan SHENG; Bin-feng ZONG

doi:10.1631/FITEE.1601358

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A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar

Regular Papers | Updated：2022-05-19

- A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar
- 一种用于多功能相控阵雷达调度的混合遗传粒子群算法
- Frontiers of Information Technology & Electronic Engineering Vol. 18, Issue 11, Pages: 1806-1816(2017)
- Affiliations：
  
  Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China
  Troop 94225, Rizhao 276000, China
  Troop 94710, Wuxi 214000, China
- Author bio：
  
  Hao-wei ZHANG, E-mail: zhw_xhzf@163.com
- Funds：
- DOI：10.1631/FITEE.1601358
  CLC： TP391
- Received：21 June 2016，
  
  Revised：2017-11-23，
  
  Published：2017-11
- Accepted：
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Hao-wei ZHANG, Jun-wei XIE, Wen-long LU, et al. 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.
DOI：

Hao-wei ZHANG, Jun-wei XIE, Wen-long LU, et al. 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. DOI： 10.1631/FITEE.1601358.

摘要

为解决多功能相控阵雷达的任务调度难题，本文提出了一种融合粒子群算法、遗传算法和启发式交错算法的优化算法。通过混沌理论优化粒子群算法的飞行参数，设计动态惯性权重，并引入遗传算法中的交叉、变异操作，使该算法的计算效率和寻优能力均得到大幅度提高。在智能算法的框架下，提出启发式的交错调度算法可进一步利用任务等待期的时间资源。仿真结果表明，与现有方法相比，本文算法效率更高，鲁棒性更强。

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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references

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