An efficient intelligent decision method for bionic motion unmanned system

2021 ◽  
pp. 095965182110655
Author(s):  
Haipeng Chen ◽  
Wenxing Fu ◽  
Yuze Feng ◽  
Jia Long ◽  
Kang Chen
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Path Planning ◽  
Motion Control ◽  
Control Method ◽  
Improved Genetic Algorithm ◽  
Decision Method ◽  
Multiple Traveling Salesman Problem ◽  
Intelligent Decision ◽  
Unmanned System

In this article, we propose an efficient intelligent decision method for a bionic motion unmanned system to simulate the formation change during the hunting process of the wolves. Path planning is a burning research focus for the unmanned system to realize the formation change, and some traditional techniques are designed to solve it. The intelligent decision based on evolutionary algorithms is one of the famous path planning approaches. However, time consumption remains to be a problem in the intelligent decisions of the unmanned system. To solve the time-consuming problem, we simplify the multi-objective optimization as the single-objective optimization, which was regarded as a multiple traveling salesman problem in the traditional methods. Besides, we present the improved genetic algorithm instead of evolutionary algorithms to solve the intelligent decision problem. As the unmanned system’s intelligent decision is solved, the bionic motion control, especially collision avoidance when the system moves, should be guaranteed. Accordingly, we project a novel unmanned system bionic motion control of complex nonlinear dynamics. The control method can effectively avoid collision in the process of system motion. Simulation results show that the proposed simplification, improved genetic algorithm, and bionic motion control method are stable and effective.

scholarly journals Multi-AGV path planning with double-path constraints by using an improved genetic algorithm

PLoS ONE ◽  
2017 ◽  
Vol 12 (7) ◽  
pp. e0181747 ◽  
Author(s):  
Zengliang Han ◽  
Dongqing Wang ◽  
Feng Liu ◽  
Zhiyong Zhao
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Improved Genetic Algorithm ◽  
Path Constraints

scholarly journals Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1472 ◽  
Author(s):  
Manuel Guerrero ◽  
Raul Baños ◽  
Consolación Gil ◽  
Francisco G. Montoya ◽  
Alfredo Alcayde
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Power Systems ◽  
Community Detection ◽  
North American ◽  
Flow Analysis ◽  
Power Grids ◽  
Improved Genetic Algorithm ◽  
Continental Scale ◽  
The North

Symmetry is a key concept in the study of power systems, not only because the admittance and Jacobian matrices used in power flow analysis are symmetrical, but because some previous studies have shown that in some real-world power grids there are complex symmetries. In order to investigate the topological characteristics of power grids, this paper proposes the use of evolutionary algorithms for community detection using modularity density measures on networks representing supergrids in order to discover densely connected structures. Two evolutionary approaches (generational genetic algorithm, GGA+, and modularity and improved genetic algorithm, MIGA) were applied. The results obtained in two large networks representing supergrids (European grid and North American grid) provide insights on both the structure of the supergrid and the topological differences between different regions. Numerical and graphical results show how these evolutionary approaches clearly outperform to the well-known Louvain modularity method. In particular, the average value of modularity obtained by GGA+ in the European grid was 0.815, while an average of 0.827 was reached in the North American grid. These results outperform those obtained by MIGA and Louvain methods (0.801 and 0.766 in the European grid and 0.813 and 0.798 in the North American grid, respectively).

scholarly journals Genetic Algorithm-Based Variable Value Control Method for Solving the Ground Target Attacking Weapon-Target Allocation Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Chao Wang ◽  
Guangyuan Fu ◽  
Daqiao Zhang ◽  
Hongqiao Wang ◽  
Jiufen Zhao
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Control Method ◽  
Search Space ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Mutation Strategy ◽  
Ground Target ◽  
Value Range ◽  
Target Allocation

Key ground targets and ground target attacking weapon types are complex and diverse; thus, the weapon-target allocation (WTA) problem has long been a great challenge but has not yet been adequately addressed. A timely and reasonable WTA scheme not only helps to seize a fleeting combat opportunity but also optimizes the use of weaponry resources to achieve maximum battlefield benefits at the lowest cost. In this study, we constructed a ground target attacking WTA (GTA-WTA) model and designed a genetic algorithm-based variable value control method to address the issue that some intelligent algorithms are too slow in resolving the problem of GTA-WTA due to the large scale of the problem or are unable to obtain a feasible solution. The proposed method narrows the search space and improves the search efficiency by constraining and controlling the variable value range of the individuals in the initial population and ensures the quality of the solution by improving the mutation strategy to expand the range of variables. The simulation results show that the improved genetic algorithm (GA) can effectively solve the large-scale GTA-WTA problem with good performance.

Parametric Programming of Multi-Type Holes Using the Improved Genetic Algorithm

2018 ◽  
Vol 764 ◽  
pp. 333-341
Author(s):  
Yong Bin Zhang ◽  
Tai Yong Wang ◽  
Yan Yu Ding ◽  
Teng Yue Shen ◽  
Jia Jia Dong ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Parametric Programming ◽  
Programming System ◽  
Improved Genetic Algorithm ◽  
Interactive Programming ◽  
Nc Code ◽  
Nc Program ◽  
Program Experiment ◽  
Cam Software

For path planning and program generation of multi-type holes, this paper presents a method of the path planning, by using the improved genetic algorithm based on chaos algorithm, and develops an interactive programming system for multi-type holes. Users can choose regular holes, and input the parameters and the necessary information, according to their needs. Then the system will combine holes automatically, and calculate the position coordinates of each hole. After that the path planning is carried out by using the improved genetic algorithm. Finally, the system carries out the simulation experiment of the path planning, and outputs NC program. Experiment shows the system can realize the path planning of multi-type holes by a combination of regular holes and generate the NC code after path planning. The optimization of path is better and the operation is more convenient than UG and other professional CAM software.

The Study of Optimal Fuzzy PID-Smith Control Based on Genetic Algorithm

2013 ◽  
Vol 313-314 ◽  
pp. 448-452
Author(s):  
Dian Ting Liu ◽  
Hai Xia Li
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Control Method ◽  
Order System ◽  
Membership Functions ◽  
Fuzzy Pid ◽  
Improved Genetic Algorithm ◽  
Simulation Results ◽  
The Stability ◽  
Better Than

In this paper, the improved genetic algorithm is applied to optimize the quantization factors and the scaling factors of fuzzy control, and the optimized rule table and membership functions is obtained according to certain performances. Then a kind of optimal fuzzy PID-Smith control method based on genetic algorithm is proposed and its simulation model is built in this paper, a second-order system is simulated and analyzed. The results show that requirements of deterministic performances of the new control method are better than the conventional methods through the simulation results in the stability, rapidity and robustness.

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