Otimização por Múltiplos Enxames Aplicada ao Escalonamento Dinâmico de Projetos de Software

In [1]:

    

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from dspsp_analysis import (algorithm_dataset, instance_dataset, comparison_dataset, 
                            max_event, metrics_dataset, mean_hypervolume)

%matplotlib inline

data = metrics_dataset()
instance_ids = list(data.instance.unique())
algorithm_ids = list(data.algorithm.unique())

instance_datasets = {}
for inst in instance_ids:
    ds = instance_dataset(data, inst, zero_values=True)
    instance_datasets[inst] = {alg: algorithm_dataset(ds, alg, zero_values=True) for alg in algorithm_ids}

comparison_datasets = {}
for inst in instance_ids:
    comparison_datasets[inst] = {alg: comparison_dataset(instance_datasets[inst][alg]) for alg in algorithm_ids}

for inst in instance_ids:
    min_event = min([max_event(ds) for ds in comparison_datasets[inst].values()])
    for k in comparison_datasets[inst].keys():
        comparison_datasets[inst][k] = comparison_datasets[inst][k][:min_event+1]


    

In [2]:

    

mean_hypervolume(data)


    

    
Out[2]:







  

    

      

      

      
mean
      
std
    
    

      
instance
      
algorithm
      

      

    
  
  

    

      
ST10_DT10_E10_SK4-5
      
NSGAII
      
0.147232
      
0.369198
    
    

      
NSGAIIDynamic
      
0.139585
      
0.369725
    
    

      
SMPSO
      
0.840048
      
0.461308
    
    

      
ST10_DT10_E15_SK4-5
      
NSGAII
      
0.072742
      
0.261099
    
    

      
NSGAIIDynamic
      
0.089373
      
0.297751
    
    

      
ST10_DT10_E5_SK4-5
      
NSGAII
      
0.175602
      
0.384316
    
    

      
NSGAIIDynamic
      
0.158632
      
0.381376
    
    

      
SMPSO
      
0.890150
      
0.535254
    
    

      
ST10_DT10_E5_SK6-7
      
NSGAII
      
0.022523
      
0.144296
    
    

      
NSGAIIDynamic
      
0.034787
      
0.200220
    
  

In [3]:

    

mean_hypervolume(data).index


    

    
Out[3]:





MultiIndex(levels=[['ST10_DT10_E10_SK4-5', 'ST10_DT10_E15_SK4-5', 'ST10_DT10_E5_SK4-5', 'ST10_DT10_E5_SK6-7'], ['NSGAII', 'NSGAIIDynamic', 'SMPSO']],
           labels=[[0, 0, 0, 1, 1, 2, 2, 2, 3, 3], [0, 1, 2, 0, 1, 0, 1, 2, 0, 1]],
           names=['instance', 'algorithm'])

In [4]:

    

comparison_datasets["ST10_DT10_E5_SK4-5"]["NSGAII"].tail()


    

    
Out[4]:







  

    

      

      
algorithm
      
event
      
hypervolume
    
  
  

    

      
177
      
NSGAII
      
177
      
0.469768
    
    

      
178
      
NSGAII
      
178
      
0.795722
    
    

      
179
      
NSGAII
      
179
      
0.895040
    
    

      
180
      
NSGAII
      
180
      
0.965737
    
    

      
181
      
NSGAII
      
181
      
0.000000
    
  

In [5]:

    

comparison_datasets["ST10_DT10_E5_SK4-5"]["NSGAIIDynamic"].tail()


    

    
Out[5]:







  

    

      

      
algorithm
      
event
      
hypervolume
    
  
  

    

      
177
      
NSGAIIDynamic
      
177
      
0.314716
    
    

      
178
      
NSGAIIDynamic
      
178
      
0.300242
    
    

      
179
      
NSGAIIDynamic
      
179
      
0.295798
    
    

      
180
      
NSGAIIDynamic
      
180
      
0.321847
    
    

      
181
      
NSGAIIDynamic
      
181
      
0.367390
    
  

In [6]:

    

comparison_datasets["ST10_DT10_E5_SK4-5"]["SMPSO"].tail()


    

    
Out[6]:







  

    

      

      
algorithm
      
event
      
hypervolume
    
  
  

    

      
177
      
SMPSO
      
177
      
0.812784
    
    

      
178
      
SMPSO
      
178
      
1.332166
    
    

      
179
      
SMPSO
      
179
      
1.236645
    
    

      
180
      
SMPSO
      
180
      
0.975271
    
    

      
181
      
SMPSO
      
181
      
1.207436
    
  

In [9]:

    

plt.title("NSGA-II x NSGA-II Dinamico")
plt.xlabel("Eventos dinâmicos (pontos de reescalonamento)")
plt.ylabel("Hipervolume")
plt.plot(comparison_datasets["ST10_DT10_E5_SK4-5"]["NSGAII"].event, comparison_datasets["ST10_DT10_E5_SK4-5"]["NSGAII"].hypervolume, "-ro", markersize=3, lw=.5, label="NSGAII")
plt.plot(comparison_datasets["ST10_DT10_E5_SK4-5"]["NSGAIIDynamic"].event, comparison_datasets["ST10_DT10_E5_SK4-5"]["NSGAIIDynamic"].hypervolume, "-go", markersize=3, lw=.5, label="NSGAIIDyn")
plt.legend()
plt.show()


    

In [ ]:

    

plt.title("SMPSO x SMPSO Dinamico")
plt.xlabel("Eventos dinâmicos (pontos de reescalonamento)")
plt.ylabel("Hipervolume")
plt.plot(datasets["smpso"].event, datasets["smpso"].hypervolume, "-ro", markersize=3, lw=.5, label="SMPSO")
plt.plot(datasets["smpso_dyn"].event, datasets["smpso_dyn"].hypervolume, "-go", markersize=3, lw=.5, label="SMPSODyn")
plt.legend()
plt.show()


    

In [ ]:

    

plt.title("NSGA-II x SMPSO")
plt.xlabel("Eventos dinâmicos (pontos de reescalonamento)")
plt.ylabel("Hipervolume")
plt.plot(datasets["nsgaii"].event, datasets["nsgaii"].hypervolume, "-ro", markersize=3, lw=.5, label="NSGAII")
plt.plot(datasets["smpso"].event, datasets["smpso"].hypervolume, "-go", markersize=3, lw=.5, label="SMPSO")
plt.legend()
plt.show()


    

In [ ]:

    

plt.title("NSGA-II Dinamico x SMPSO Dinamico")
plt.xlabel("Eventos dinâmicos (pontos de reescalonamento)")
plt.ylabel("Hipervolume")
plt.plot(datasets["nsgaii_dyn"].event, datasets["nsgaii_dyn"].hypervolume, "-ro", markersize=3, lw=.5, label="NSGAIIDyn")
plt.plot(datasets["smpso_dyn"].event, datasets["smpso_dyn"].hypervolume, "-go", markersize=3, lw=.5, label="SMPSODyn")
plt.legend()
plt.show()


    

In [ ]: