Improved Multi-objective Swarm Algorithm to Optimize Wash-out Motion and its Simulation Experiment

doi:10.16182/j.issn1004731x.joss.22-1193

Abstract

Abstract:

Addressing the issues such as signal loss, distraction, and bad wash-out effect caused by improper parameter selection in classic wash-out algorithms, an improved multi-objective artificial bee colony algorithm is proposed to optimize the filter parameters of the classical wash-out algorithm to improve the effect. For the problems in the initialization and local optimization of traditional swarm algorithm, Circle mapping and Pareto local optimization algorithm are introduced. The human perception error model, acceleration difference model, and displacement model are established, and the model function is used as the objective function, the parameters of the classical wash-out algorithm is optimized by the improved multi-objective artificial bee colony algorithm. A simulation model is established to simulate and verify the optimized wash-out algorithm, and a flight simulator motion test platform is applied to test and verify the algorithm. The results show that, with the optimized washout algorithm, the washout fidelity is effectively improved, the error peak is reduced, the phase delay is improved, and the motion space is saved.

Key words: multi-objective optimization, artificial bee colony algorithm, wash-out algorithm, parameter optimization, dynamic fidelity

CLC Number:

TP391.9

Wang Hui, Peng Le. Improved Multi-objective Swarm Algorithm to Optimize Wash-out Motion and its Simulation Experiment[J]. Journal of System Simulation, 2024, 36(2): 436-448.

Figures/Tables 24

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 1

Table 2

Fig. 5

Fig. 6

Fig. 7

Table 3

Optimized filter parameters

方向	$ω a h$	$ω a l$	$ω ω h$
纵向x	3.60	3.54	3.10
横向y	3.60	3.54	3.10
垂向z	3.11	―	2.05

Table 3

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

Fig. 14

Fig. 15

Fig. 16

Fig. 17

Fig. 18

Table 4

Table 5

Table 6

References 16

1	王辉, 吕兴顺. 一种改进的萤火虫算法及在洗出优化中的应用[J]. 系统仿真学报, 2021, 33(2): 306-314.
	Wang Hui, Xingshun Lü. An Improved Firefly Algorithm and its Application in Washout Optimization[J]. Journal of System Simulation, 2021, 33(2): 306-314.
2	王小亮, 李立, 张卫华. 列车驾驶模拟器经典洗出算法的参数优化[J]. 中国铁道科学, 2008, 29(5): 102-107.
	Wang Xiaoliang, Li Li, Zhang Weihua. Parameters Optimization of the Classical Washout Algorithm in Locomotive Driving Simulator[J]. China Railway Science, 2008, 29(5): 102-107.
3	王辉, 阿迪娜. 改进鱼群算法在模拟机运动洗出优化中的应用[J]. 计算机系统应用, 2022, 31(8): 265-272.
	Wang Hui, Dina A. Application of Improved Fish Swarm Algorithm in Optimization of Simulator Motion Washout[J]. Computer Systems & Applications, 2022, 31(8): 265-272.
4	刘伟超, 王辉. 基于运动试验平台的MOEA/D改进洗出算法仿真及试验[J]. 重庆大学学报, 2023, 46(1): 125-136.
	Liu Weichao, Wang Hui. Simulation and Experiments of MOEA/D Improved Washout Algorithm Based on Motion Test Platform[J]. Journal of Chongqing University, 2023, 46(1): 125-136.
5	Karaboga D. An Idea Based on Honey Bee Swarm for Numerical Optimization[R]. Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
6	Hedayatzadeh Ramin, Hasanizadeh Bahareh, Akbari Reza, et al. A Multi-objective Artificial Bee Colony for Optimizing Multi-objective Problems[C]//2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE). Piscataway, NJ, USA: IEEE, 2010: V5-277-V5-281.
7	Seyed Alireza Mohammadi, Mohammad Reza Feizi Derakhshi, Akbari Reza. An Adaptive Multi-objective Artificial Bee Colony with Crowding Distance Mechanism[C]//The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012). Piscataway, NJ, USA: IEEE, 2012: 68-73.
8	Akay Bahriye. Synchronous and Asynchronous Pareto-based Multi-objective Artificial Bee Colony Algorithms[J]. Journal of Global Optimization, 2013, 57(2): 415-445.
9	单娴, 杜学东. 基于复数编码的多策略人工蜂群算法[J]. 系统工程学报, 2018, 33(5): 597-605.
	Shan Xian, Du Xuedong. Multi-strategy Artificial Bee Colony Algorithm Based on Complex Coding[J]. Journal of Systems Engineering, 2018, 33(5): 597-605.
10	赵新秋, 段思雨, 马学敏. 基于调节算子的多目标人工蜂群算法[J]. 系统工程学报, 2021, 36(5): 602-611.
	Zhao Xinqiu, Duan Siyu, Ma Xuemin. Multi-objective Artificial Bee Colony Algorithm Based on Regulation Operators[J]. Journal of Systems Engineering, 2021, 36(5): 602-611.
11	赵新秋, 段思雨, 马学敏. 基于阈值搜索的多目标人工蜂群算法[J]. 控制与决策, 2020, 35(8): 1793-1802.
	Zhao Xinqiu, Duan Siyu, Ma Xuemin. A Multi-objective Artificial Bee Colony Based on Limit Search Strategy[J]. Control and Decision, 2020, 35(8): 1793-1802.
12	张禹, 公健, 唐滋阳, 等. 基于改进多目标人工蜂群算法的航空发动机管路智能布局方法[J]. 机械工程学报, 2022, 58(4): 277-284.
	Zhang Yu, Gong Jian, Tang Ziyang, et al. Method for Intelligent Aeroengine Pipeline Layout Based on Improved Multi-objective Artificial Bee Colony Algorithm[J]. Journal of Mechanical Engineering, 2022, 58(4): 277-284.
13	万鹏飞, 高兴宝. 一种解多目标优化问题的基于分解的人工蜂群算法[J]. 山东大学学报(理学版), 2018, 53(11): 56-66, 77.
	Wan Pengfei, Gao Xingbao. Novel Artificial Bee Colony Algorithm Based on Objective Space Decomposition for Solving Multi-objective Optimization Problems[J]. Journal of Shandong University(Natural Science), 2018, 53(11): 56-66, 77.
14	Karaboga D, Basturk B. On the Performance of Artificial Bee Colony (ABC) Algorithm[J]. Applied Soft Computing, 2008, 8(1): 687-697.
15	Xiang Xiaohan, Yan Xiaoming, Gao Chuyi, et al. A Circle Chaos Random Search Strategy Particle Swarm Optimization with Its Application[J]. Computers and Electrical Engineering, 2022, 102: 108219.
16	刘伟超, 王辉. 飞行模拟器洗出算法多重滤波信号补偿优化研究[J]. 系统仿真学报, 2023, 35(1): 137-145.
	Liu Weichao, Wang Hui. Research on Multiple Filter Signal Compensation for Washout Algorithm Optimization of Flight Simulator[J]. Journal of System Simulation, 2023, 35(1): 137-145.

算法	UF7	CF1
CPMOABC	0.068 2	0.009 3
MOABC	0.317 9	0.142 6
MOEA/D	0.083 3	0.033 6

算法	UF7	CF1
CPMOABC	3.654 9	3.724 4
MOABC	3.236 9	3.339 3
MOEA/D	3.652 2	3.584 2

算法	max	min	伸缩量	节省空间/%
EOFA	72.783 8	51.732 3	21.051 5	23.2
GSAFSA	72.591 3	52.696 9	19.894 4	28.9
CPMOABC	72.555 0	55.358 4	17.196 6

算法	max	min	伸缩量	节省空间/%
EOFA	71.967 5	20.489 4	51.478 1	20.6
GSAFSA	71.878 7	22.759 4	49.119 3	9.0
CPMOABC	72.781 7	27.457 6	45.324 1

算法	max	min	伸缩量	节省空间/%
EOFA	140.313 0	71.525 9	68.787 1	15.6
GSAFSA	137.771 0	71.621 9	66.149 1	12.3
CPMOABC	129.630 0	71.605 9	58.024 1