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Modeling and optimization of multiple unmanned aerial vehicles system architecture alternatives.

Qin D, Li Z, Yang F, Wang W, He L - ScientificWorldJournal (2014)

Bottom Line: This can be extracted as a multi UAVs system architecture problem.Based on the general system architecture problem, a specific description of the multi UAVs system architecture problem is presented.The availability and effectiveness of overall method is validated using 2 simulations based on 2 different scenarios.

View Article: PubMed Central - PubMed

Affiliation: College of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, China.

ABSTRACT
Unmanned aerial vehicle (UAV) systems have already been used in civilian activities, although very limitedly. Confronted different types of tasks, multi UAVs usually need to be coordinated. This can be extracted as a multi UAVs system architecture problem. Based on the general system architecture problem, a specific description of the multi UAVs system architecture problem is presented. Then the corresponding optimization problem and an efficient genetic algorithm with a refined crossover operator (GA-RX) is proposed to accomplish the architecting process iteratively in the rest of this paper. The availability and effectiveness of overall method is validated using 2 simulations based on 2 different scenarios.

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The general evolutionary process.
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fig3: The general evolutionary process.

Mentions: What is the architect trying to achieve? What makes an architecture “good?” To answer this question, a process is needed for selecting architectures that are not only capable of accomplishing the demand tasks but are also effective in terms of their overall value acquired. The quality of the proposed architecture is evaluated according to some criteria, which is the overall value acquired by the UAVs through conducting the tasks in this paper. It is inevitable for architect that the selecting and assigning should be done iteratively. The system architecting problem of multi-UAVs can be formulated as a constrained, combinatorial optimization problem. For example, these problems can be formulated as generalized assignment problems, quadratic assignment problems, or their expansions. As excellent population-based search algorithms, evolutionary algorithms (e.g., genetic algorithms, particle swarm optimization) can be chosen to solve these multi-UAVs system architecting problems. The general process of solving multi-UAVs system architecting problems employing evolutionary algorithms is presented in Figure 3. An optimization problem and an efficient genetic algorithm with a refined crossover operator (GA-RX) are proposed to accomplish the architecting process iteratively in the rest of this paper.


Modeling and optimization of multiple unmanned aerial vehicles system architecture alternatives.

Qin D, Li Z, Yang F, Wang W, He L - ScientificWorldJournal (2014)

The general evolutionary process.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4129171&req=5

fig3: The general evolutionary process.
Mentions: What is the architect trying to achieve? What makes an architecture “good?” To answer this question, a process is needed for selecting architectures that are not only capable of accomplishing the demand tasks but are also effective in terms of their overall value acquired. The quality of the proposed architecture is evaluated according to some criteria, which is the overall value acquired by the UAVs through conducting the tasks in this paper. It is inevitable for architect that the selecting and assigning should be done iteratively. The system architecting problem of multi-UAVs can be formulated as a constrained, combinatorial optimization problem. For example, these problems can be formulated as generalized assignment problems, quadratic assignment problems, or their expansions. As excellent population-based search algorithms, evolutionary algorithms (e.g., genetic algorithms, particle swarm optimization) can be chosen to solve these multi-UAVs system architecting problems. The general process of solving multi-UAVs system architecting problems employing evolutionary algorithms is presented in Figure 3. An optimization problem and an efficient genetic algorithm with a refined crossover operator (GA-RX) are proposed to accomplish the architecting process iteratively in the rest of this paper.

Bottom Line: This can be extracted as a multi UAVs system architecture problem.Based on the general system architecture problem, a specific description of the multi UAVs system architecture problem is presented.The availability and effectiveness of overall method is validated using 2 simulations based on 2 different scenarios.

View Article: PubMed Central - PubMed

Affiliation: College of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, China.

ABSTRACT
Unmanned aerial vehicle (UAV) systems have already been used in civilian activities, although very limitedly. Confronted different types of tasks, multi UAVs usually need to be coordinated. This can be extracted as a multi UAVs system architecture problem. Based on the general system architecture problem, a specific description of the multi UAVs system architecture problem is presented. Then the corresponding optimization problem and an efficient genetic algorithm with a refined crossover operator (GA-RX) is proposed to accomplish the architecting process iteratively in the rest of this paper. The availability and effectiveness of overall method is validated using 2 simulations based on 2 different scenarios.

Show MeSH