fix: Fixed seed and multiproc bug

fliscopt/__init__.py

@@ -1,3 +1,3 @@
 from . import ga,hc,rs,sa,multiproc,chaining,base_algorithm,fitness,utils
 
-__version__ = "0.1.4"
+__version__ = "0.1.5"

fliscopt/base_algorithm.py

@@ -16,11 +16,11 @@ def __init__(self, domain, fitness_function,seed=random.randint(10, 100),seed_in
         self.max_time=1000
         if self.seed_init:
             # Set the seed for initial population only
-            self.r_init = random.Random(seed)
+            self.r_init = random.Random(self.seed)
         else:
             # Same seeds for both init and other random generators
-            self.r_init = random.Random(seed)
-            random.seed(seed)
+            self.r_init = random.Random(self.seed)
+            random.seed(self.seed)
 
         self.best_cost =0.0  # returned
     @abstractmethod

fliscopt/ga.py

@@ -3,7 +3,7 @@
 import time
 sys.path.append(os.getcwd())
 from .utils.util import plot_scores, print_schedule, read_file
-from .base_algorithm import FlightAlgorithm
+from .base_algorithm import FlightAlgorithm,random
 from .rs import RandomSearch
 from .utils.ga_utils import crossover, mutation
 from .fitness import *
@@ -41,6 +41,7 @@ def run(self,domain,fitness_function,seed) -> tuple:
 
 
 
+
 class GA(BaseGA):
     def __init__(self, domain=domain['domain'], fitness_function=fitness_function, seed=random.randint(10, 100), seed_init=True, init=[],max_time=100,
                  population_size=100, step=1, probability_mutation=0.2, elitism=0.2,
@@ -49,9 +50,7 @@ def __init__(self, domain=domain['domain'], fitness_function=fitness_function, s
                          0, elitism, number_generations, search)
 
     def run(self,domain,fitness_function,seed) -> tuple:
-        self.domain = domain
-        self.fitness_function = fitness_function
-        self.seed = seed
+        self.__init__(domain, fitness_function, seed, self.seed_init, self.init,self.max_time)
         population = []
         scores = []
         nfe = 0
@@ -97,7 +96,7 @@ def run(self,domain,fitness_function,seed) -> tuple:
                     i1 = random.randint(0, number_elitism)
                     i2 = random.randint(0, number_elitism)
                     population.append(
-                        crossover(domain, ordered_individuals[i1], ordered_individuals[i2]))
+                        crossover(self.domain, ordered_individuals[i1], ordered_individuals[i2]))
 
             if time.time()-self.start_time>self.max_time:
                 return costs[0][1], costs[0][0], scores, nfe, self.seed
@@ -113,9 +112,7 @@ def __init__(self, domain=domain['domain'], fitness_function=fitness_function, s
                          probability_crossover, elitism, number_generations, search)
 
     def run(self,domain,fitness_function,seed) -> tuple:
-        self.domain = domain
-        self.fitness_function = fitness_function
-        self.seed = seed
+        self.__init__(domain, fitness_function, seed, self.seed_init, self.init,self.max_time)
         population = []
         scores = []
         nfe = 0
@@ -157,7 +154,7 @@ def run(self,domain,fitness_function,seed) -> tuple:
                     i1 = random.randint(0, number_elitism)
                     i2 = random.randint(0, number_elitism)
                     population.append(
-                        crossover(domain, ordered_individuals[i1], ordered_individuals[i2]))
+                        crossover(self.domain, ordered_individuals[i1], ordered_individuals[i2]))
                 else:
                     m = random.randint(0, number_elitism)
                     population.append(
@@ -169,7 +166,6 @@ def run(self,domain,fitness_function,seed) -> tuple:
         return costs[0][1], costs[0][0], scores, nfe, self.seed
 
 
-
 class GAReversals(BaseGA):
     def __init__(self, domain=domain['domain'], fitness_function=fitness_function, seed=random.randint(10, 100), seed_init=True, init=[],max_time=100,
                  population_size=100, step=1, probability_mutation=0.2, elitism=0.2,
@@ -180,9 +176,7 @@ def __init__(self, domain=domain['domain'], fitness_function=fitness_function, s
         self.step_length = step_length
 
     def run(self,domain,fitness_function,seed) -> tuple:
-        self.domain = domain
-        self.fitness_function = fitness_function
-        self.seed = seed
+        self.__init__(domain, fitness_function, seed, self.seed_init, self.init,self.max_time)
         population = []
         scores = []
         nfe = 0
@@ -261,8 +255,8 @@ def run(self,domain,fitness_function,seed) -> tuple:
                     return costs[0][1], costs[0][0], scores, nfe, self.seed
 
         return costs[0][1], costs[0][0], scores, nfe, self.seed
-            
-
+
+ 
 if __name__ == '__main__':
     read_file('flights.txt')
     sga = ReverseGA(seed_init=False,search=True)

fliscopt/hc.py

@@ -4,9 +4,9 @@
 import time
 from abc import ABCMeta
 from .utils.util import plot_scores, print_schedule, read_file
-from .base_algorithm import FlightAlgorithm
+from .base_algorithm import FlightAlgorithm,random
 
-import random
+#import random
 from .fitness import *
 
 class HillClimb(FlightAlgorithm,metaclass=ABCMeta):
@@ -22,12 +22,11 @@ def get_name(self) -> str:
         return self.__class__.__name__
 
     def run(self,domain,fitness_function,seed) -> tuple:
+        self.__init__(domain,fitness_function,seed,self.seed_init, self.init,self.max_time)
         count = 0
         scores = []
         nfe = 0
-        self.domain=domain
-        self.fitness_function=fitness_function
-        self.seed=seed
+
         if len(self.init) > 0:
             solution = self.init
         else:

fliscopt/multiproc.py

@@ -1,6 +1,6 @@
 import os
 import sys
-sys.path.append(os.getcwd())
+sys.path.append("..")
 
 
 import multiprocessing
@@ -69,5 +69,6 @@ def multiple_runs(algorithm, domain, fitness_function, init=[], record=False, n_
 
 if __name__ == '__main__':
     read_file('flights.txt')
-    multiple_runs(ReverseGA, domain, fitness_function, record=False, n=10)
+    multiple_runs(ReverseGA, domain['griewank']*12, griewank, record=False, n=10)
+
 

fliscopt/rs.py

@@ -27,9 +27,7 @@ def get_name(self) -> str:
 
 
     def run(self,domain,fitness_function,seed):
-            self.domain=domain
-            self.fitness_function=fitness_function
-            self.seed=seed
+            self.__init__(domain,fitness_function,seed,self.seed_init, self.init,self.max_time)
             scores = []
             nfe = 0
             if len(self.init) > 0:
@@ -55,6 +53,7 @@ def run(self,domain,fitness_function,seed):
                 if time.time()-self.start_time>self.max_time:
                     return self.best_solution, self.best_cost, scores, nfe, self.seed
             return self.best_solution, self.best_cost, scores, nfe, self.seed
+
 
 if __name__ == '__main__':
     read_file('flights.txt')

fliscopt/sa.py

@@ -4,11 +4,11 @@
 sys.path.append(os.getcwd())
 from abc import ABCMeta
 from .utils.util import plot_scores, print_schedule, read_file
-from .base_algorithm import FlightAlgorithm
+from .base_algorithm import FlightAlgorithm,random
 import heapq
 import math
 
-import random
+#import random
 from .fitness import *
 
 class SimulatedAnnealing(FlightAlgorithm,metaclass=ABCMeta):
@@ -28,9 +28,7 @@ def get_name(self) -> str:
         return self.__class__.__name__
 
     def run(self,domain,fitness_function,seed) -> tuple:
-        self.domain=domain
-        self.fitness_function=fitness_function
-        self.seed=seed
+        self.__init__(domain,fitness_function,seed,self.seed_init, self.init,self.max_time)
         count = 0
         nfe = 0
         scores = []