Pso updated, Lib updated, main updated

PSO_Lib.py

@@ -18,14 +18,14 @@ def getDistance(lat1,lon1,lat2,lon2):
     dy = 111.3*(lat1-lat2)
     return math.sqrt(dx*dx+dy*dy)
 
-def costFunction(sample,permutation):
+def cost_function(sample,permutation):
     cost = 0
     for i in range(0, len(permutation)-1):
-        lat1 = sample['Breitengrad_DEC'][sample.index[permutation[i]]]
-        lon1 = sample['Laengengrad_DEC'][sample.index[permutation[i]]]
-        lat2 = sample['Breitengrad_DEC'][sample.index[permutation[i+1]]]
-        lon2 = sample['Laengengrad_DEC'][sample.index[permutation[i+1]]]
-        cost += getDistance(lat1,lon1,lat2,lon2)
+        lat1 = sample[permutation[i]][1]
+        lon1 = sample[permutation[i]][2]
+        lat2 = sample[permutation[i+1]][1]
+        lon2 = sample[permutation[i+1]][2]
+        cost += math.ceil(getDistance(lat1,lon1,lat2,lon2))
     return cost
 
 def swap(liste, x, y): 
@@ -38,16 +38,16 @@ def swap(liste, x, y):
             continue
     return liste
   
-def add_pos_geschw(p1,v):
+def add_pos_vel(p1,v):
     p2=p1
     for i in v:
         p2 = swap(p2,i[0],i[1])
     return p2
 
-def add_geschw(v1,v2):
+def add_vel(v1,v2):
     return v1+v2
 
-def sub_position(p1,p2):
+def sub_pos(p1,p2):
     v =list()
     temp1=0
     temp2=0
@@ -59,9 +59,8 @@ def sub_position(p1,p2):
             v.append((temp1,temp2))
     return v
 
-def multipl_koeff_geschw(c,v):
+def multipl_coeff_vel(c,v):
     newLen = int(np.floor(c*len(v)))
     v2 = v[:newLen]
     return v2
-    
     
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Particle.py

+
+import numpy as np
+
 class Particle():
-    pass
+    
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+    def __init__(self,n):
+        range_array= list(np.arange(0,n))
+        np.random.shuffle(range_array)
+        range_array.append(range_array[0])
+        self.position = range_array
+        self.cost = float('inf')
+        self.local_attractor = self.position
+        self.local_attractor_cost = float('inf')
+        self.velocity = list()
+        
+    def print_particle(self,i):
+        print("Particle Nr.: ", i )
+        print("Tour: ", self.position, "Kosten: ", self.cost)
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PsoLars.py

+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jan  9 11:00:23 2021
+
+@author: larsw
+"""
+
+import Particle as part
+import PSO_Lib as pl
+import random
+import pandas as pd
+
+
+
+
+
+    
+
+def pso (n, pop, sample, c1=1/3, c2=1/3, c3=1/3, abort = 50):
+    test_array= list()
+    global_attractor = list()
+    global_attractor_cost = float('inf')
+    
+    ##### Initialize #####
+    
+    population = list()
+    
+    # create pop-many particle
+    
+    for i in range(pop):
+        new_particle = part.Particle(n)
+        population.append(new_particle)
+        for p in population:
+            p.local_attractor=p.position
+            p.local_attractor_cost = pl.cost_function(sample,p.local_attractor)
+        
+            
+            if(p.local_attractor_cost < global_attractor_cost):
+                global_attractor = p.position
+                global_attractor_cost = p.local_attractor_cost
+            
+                
+     
+    # Main-Loop    
+    for i in range(abort):
+        
+        # Calculate new velocity and position for each particle
+        for p in population:
+            
+            p.velocity = pl.add_vel(
+                    pl.multipl_coeff_vel(c1,p.velocity),
+                    pl.add_vel(
+                        pl.multipl_coeff_vel(
+                            c2,pl.sub_pos(p.local_attractor,p.position)),
+                        pl.multipl_coeff_vel(
+                            c3,pl.sub_pos(global_attractor,p.position))
+                    ))
+            
+            p.position = pl.add_pos_vel(p.position,p.velocity)
+            p.cost = pl.cost_function(sample,p.position)
+           
+            
+            if(p.cost<p.local_attractor_cost):
+                p.local_attractor=p.position
+                p.local_attractor_cost=p.cost
+         
+            if(p.cost<global_attractor_cost):
+               
+                global_attractor=p.position
+                global_attractor_cost = p.cost
+       
+        print(global_attractor)
+     
+        
+            
+    return test_array
+

main.py

+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jan  9 11:11:44 2021
+
+@author: larsw
+"""
+
+import Particle as part
+import PSO_Lib  as pl
+import PsoLars as pso
+import pandas as pd
+
+df = pd.read_excel('Koordinaten.xlsx')
+
+
+# Random trip
+#sample = df.sample(5)
+#pso.pso(n=5,pop =10,sample=sample)
+
+# Route : Berlin, Braunschweig, Dresden, Bonn, Konstanz, Nürnberg, Hamburg
+
+staedte_liste = ['Berlin', 'Braunschweig', 'Dresden', 'Bonn', 'Konstanz', 'Nürnberg', 'Hamburg']
+
+df_set = pd.DataFrame(columns=df.columns)
+samp = df.sample(100)
+for i in staedte_liste:
+    df_set=df_set.append(df.loc[df['Staedte']==i])
+    
+b = df_set.drop(['Unnamed: 0', 'Breitengrad', 'Laengengrad'], axis=1)
+
+a = b.values.tolist()
+
+print(a)
+
+pso.pso(len(a),40,sample=a,abort = 50)
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