Multi-UAV-Task-Assignment-Benchmark | UAV Task Allocation of an Extended Team Orienteering Problem
kandi X-RAY | Multi-UAV-Task-Assignment-Benchmark Summary
kandi X-RAY | Multi-UAV-Task-Assignment-Benchmark Summary
Multi-UAV-Task-Assignment-Benchmark is a Python library. Multi-UAV-Task-Assignment-Benchmark has no bugs, it has no vulnerabilities and it has low support. However Multi-UAV-Task-Assignment-Benchmark build file is not available. You can download it from GitHub.
A benchmark for multi-UAV task assignment is presented in order to evaluate different algorithms. An extended Team Orienteering Problem is modeled for a kind of multi-UAV task assignment problem. Three intelligent algorithms, i.e., Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization are implemented to solve the problem. A series of experiments with different settings are conducted to evaluate three algorithms. The modeled problem and the evaluation results constitute a benchmark, which can be used to evaluate other algorithms used for multi-UAV task assignment problems. Please refer to the paper to see more detail. Xiao, K., Lu, J., Nie, Y., Ma, L., Wang, X., Wang, G.: A Benchmark for Multi-UAV Task Assignment of an Extended Team Orienteering Problem. arXiv preprint arXiv:2009.00363 (2020).
A benchmark for multi-UAV task assignment is presented in order to evaluate different algorithms. An extended Team Orienteering Problem is modeled for a kind of multi-UAV task assignment problem. Three intelligent algorithms, i.e., Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization are implemented to solve the problem. A series of experiments with different settings are conducted to evaluate three algorithms. The modeled problem and the evaluation results constitute a benchmark, which can be used to evaluate other algorithms used for multi-UAV task assignment problems. Please refer to the paper to see more detail. Xiao, K., Lu, J., Nie, Y., Ma, L., Wang, X., Wang, G.: A Benchmark for Multi-UAV Task Assignment of an Extended Team Orienteering Problem. arXiv preprint arXiv:2009.00363 (2020).
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Multi-UAV-Task-Assignment-Benchmark has a low active ecosystem.
It has 68 star(s) with 21 fork(s). There are 2 watchers for this library.
It had no major release in the last 6 months.
There are 2 open issues and 3 have been closed. On average issues are closed in 1 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Multi-UAV-Task-Assignment-Benchmark is current.
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Multi-UAV-Task-Assignment-Benchmark has no bugs reported.
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Multi-UAV-Task-Assignment-Benchmark has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
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Multi-UAV-Task-Assignment-Benchmark does not have a standard license declared.
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Multi-UAV-Task-Assignment-Benchmark releases are not available. You will need to build from source code and install.
Multi-UAV-Task-Assignment-Benchmark has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions are not available. Examples and code snippets are available.
Top functions reviewed by kandi - BETA
kandi has reviewed Multi-UAV-Task-Assignment-Benchmark and discovered the below as its top functions. This is intended to give you an instant insight into Multi-UAV-Task-Assignment-Benchmark implemented functionality, and help decide if they suit your requirements.
- Runs the simulation
- Find the positions in X
- Performs variation function
- Calculates the real flight data
- Function for the transfer function
- Function to calculate the fitness of a vehicle
- Iterate over the ring algorithm
- Evaluate vehicle
- Visualize the task distribution
- Reset the vehicle
- Run the program
- Calculates the total reward
- Run a single gene
- Random selection
- Random mutation
- Perform crossover
- Compute fitness for a given target
- Run the ACO algorithm
- Returns the distance matrix
- Return the test function
Get all kandi verified functions for this library.
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Install Multi-UAV-Task-Assignment-Benchmark
You can download it from GitHub.
You can use Multi-UAV-Task-Assignment-Benchmark like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use Multi-UAV-Task-Assignment-Benchmark like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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