首页|基于多策略改进蜣螂算法的推力分配应用设计

基于多策略改进蜣螂算法的推力分配应用设计

扫码查看
推力分配求解是复杂的非线性约束优化问题.传统推力分配算法在处理该类问题时存在精度低及易陷入局部极值点等问题,而群智能算法能够较容易解决这些问题,但需要解决稳定性和快速收敛性问题.针对上述问题,提出一种改进蜣螂推力分配算法(IDBO),该算法通过选取解系数为个体变量、种群初始化采取拉丁超立方和反向学习法、蜣螂跳舞行为位置及最优蜣螂位置更新策略设计,提高算法收敛速度和稳定性.仿真结果表明,该算法收敛性速度优于所对比群智能算法,推力分配精度和能耗也明显优于所有对比算法.
Application Design of Thrust Allocation Based on Multi Strategy Improved Dung Beetle Algorithm
The solution of thrust allocation is a complex nonlinear constrained optimization problem.Traditional thrust allocation algorithms have low accuracy and are prone to falling into local extreme points when dealing with such problems.Although swarm intelligence algorithms can easily solve these problems,they need to solve stability and fast convergence problems.To solve them,an improved dung beetle thrust allocation algorithm(IDBO)is proposed.It im-proves the convergence speed and stability of the algorithm by selecting solution coefficients as individual variables,using Latin hypercube and reverse learning methods for population initialization,improving the position update strategy for dung beetle dancing behavior,and designing the optimal dung beetle position update strategy.The simulation re-sults show that the convergence speed of it is superior to the compared swarm intelligence algorithms,and the thrust allocation accuracy and energy consumption are also significantly better than all the comparison algorithms.

thrust allocationdung beetle algorithmdynamic positioningLatin hypercube

刘明、娄德成、王晓飞

展开 >

南通大学 杏林学院,南通 226236

招商局重工(江苏)有限公司,南通 226116

推力分配 蜣螂算法 动力定位 拉丁超立方

江苏省自然科学基金项目南通市科技计划项目

BK20180953JC22022085

2024

自动化与仪表
天津市工业自动化仪表研究所 天津市自动化学会

自动化与仪表

CSTPCD
影响因子:0.548
ISSN:1001-9944
年,卷(期):2024.39(8)
  • 6