83 lines
1.1 KiB
Markdown
83 lines
1.1 KiB
Markdown
# EMOA
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Benchmarks of Evolutionary multi-objective optimization algorithms (EMOA) on Real-world Multi-objective
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Optimization Problem Suite.
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## Quick Start
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Creating the environment：
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```
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conda create -n pymoo python=3.8
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conda activate pymoo
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pip install -U pymoo
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```
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Change the test problem in `main.py`
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```
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python main.py
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```
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## Benchmark
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> population size: 100
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> number of generations: 200
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#### CRE-2-3-1
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```
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Time (s):
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CTAEA 6.297407865524292
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NSGA2 9.709688425064087
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NSGA3 13.19536280632019
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```
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#### CRE-2-4-2
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```
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Time (s):
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CTAEA 5.5211687088012695
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NSGA2 8.863621950149536
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NSGA3 12.693290948867798
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```
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#### CRE-2-4-3
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```
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Time (s):
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CTAEA 5.683619022369385
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NSGA2 9.352391481399536
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NSGA3 13.239986419677734
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```
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#### CRE-2-7-4
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```
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Time (s):
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CTAEA 6.6659016609191895
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NSGA2 10.643601417541504
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NSGA3 14.66565227508545
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```
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#### CRE-2-4-5
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```
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Time (s):
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CTAEA 5.434146165847778
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NSGA2 10.283865928649902
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NSGA3 15.255037069320679
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```
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