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maximization_example   A
last analyzed

↳ Parent: Project

Complexity

Total Complexity 0

Size/Duplication

Total Lines 19
Duplicated Lines 0 %

Importance

Changes 0
Metric Value
eloc 12
dl 0
loc 19
rs 10
c 0
b 0
f 0
wmc 0
1 
# encoding=utf8
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# This is temporary fix to import module from parent folder
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# It will be removed when package is published on PyPI
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import sys
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sys.path.append('../')
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# End of fix
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import random

            


            
                                                        
            
                                    
            
            

                
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from NiaPy.algorithms.basic import ParticleSwarmAlgorithm

            


            
                                                        
            
                                    
            
            

                
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from NiaPy.task import StoppingTask, OptimizationType

            


            
                                                        
            
                                    
            
            

                
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from NiaPy.benchmarks import Sphere

            


            
                                                        
            
                                    
            
            

                
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#we will run ParticleSwarmAlgorithm for 5 independent runs

            


            
                                                        
            
                                    
            
            

                
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for i in range(5):

            


            
                                                        
            
                                    
            
            

                
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    task = StoppingTask(D=10, nFES=1000, benchmark=Sphere())

            


            
                                                        
            
                                    
            
            

                
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    algo = ParticleSwarmAlgorithm(NP=40, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4)

            


            
                                                        
            
                                    
            
            

                
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    best = algo.run(task=task)

            


            
                                                        
            
                                    
            
            

                
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    print (best)

            


            
                                                        
            
                                    
            
            

                
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