Resolving bug that removes certain solutions from the best front if the best front is larger than the half of the population

Author: B-Moritz

alg/NSGA_II/base_nsga_ii.py

@@ -35,8 +35,9 @@ def __init__(self,
 
 
     def print_performance(self, cur_result: dict):
-        print(f"{cur_result['Iteration']} : Makespan: [min : {cur_result['Makespan']['Min']:.2f}, avg : {cur_result['Makespan']['Avg']:.2f}], " + \
-            f"Mean flow time: [min : {cur_result['Mean flow time']['Min']:.2f}, avg : {cur_result['Mean flow time']['Avg']:.2f}], " + \
+        objective_1, objective_2, *_ =  list(cur_result)
+        print(f"{cur_result['Iteration']} : {objective_1}: [min : {cur_result[objective_1]['Min']:.2f}, avg : {cur_result[objective_1]['Avg']:.2f}], " + \
+            f"{objective_2}: [min : {cur_result[objective_2]['Min']:.2f}, avg : {cur_result[objective_2]['Avg']:.2f}], " + \
                 f"Spread : {cur_result['Spread']}, n_fronts: {cur_result['n_fronts']}, n_non_dominated_solutions: {cur_result['n_non_dominated_solutions']}")
 
     def dump_population(self, dump_folder, file_name):
@@ -150,8 +151,8 @@ def save_objective_space_plot(self, log_path: str, figure_name: str, folder_name
         ax.set_title(figure_name)
         #ax.set_xticks(np.linspace(np.min(point_list[:, 0])-10, np.max(point_list[:, 0])+10, 10))
         #ax.set_yticks(np.linspace(np.min(point_list[:, 1])-10, np.max(point_list[:, 1])+10, 10))
-        ax.set_xlabel("Makespan")
-        ax.set_ylabel("Mean Flow Time")
+        ax.set_xlabel(self.pop_object.objectives[0])
+        ax.set_ylabel(self.pop_object.objectives[1])
 
         fig.savefig(os.path.join(img_folder, figure_name))
         plt.close()
@@ -198,6 +199,12 @@ def execute(self):
         self.pop_object.non_dominated_sorting()
         self.pop_object.crowding_distance_sort_all_fronts()
         max_iteration = self.n_iterations
+
+        previous_makespan_min = np.inf
+        previous_flow_min = np.inf
+        cur_makespan_ind = None
+        cur_flow_ind = None
+
         while self.n_iterations > 0:
             # Higher tournament size -> more elitism, smaller torunament size -> less elitism
             self.pop_object.select_parents()
@@ -213,6 +220,31 @@ def execute(self):
             cur_result["Iteration"] = max_iteration - self.n_iterations
             self.print_performance(cur_result)
 
+            # Debug help
+            #cur_min_makespan = cur_result["Makespan"]["Min"]
+            #cur_min_mean_flow_time = cur_result["Mean Completion Time"]["Min"]
+            #if cur_min_makespan > previous_makespan_min or cur_min_mean_flow_time > previous_flow_min:
+            #    print("----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------")
+            #    self.pop_object.non_dominated_sorting()
+            #    self.pop_object.crowding_distance_sort_all_fronts()
+                
+            #min_makespan_found = False
+            #min_mean_flow_time_found = False
+            #for indiv in self.pop_object.R:
+            #    if not min_makespan_found and indiv.cur_fitness[0] == cur_min_makespan:
+            #        min_makespan_found = True
+            #        indiv.watch_individual = True
+            #        cur_makespan_ind = indiv
+            #    elif not min_mean_flow_time_found and indiv.cur_fitness[1] == cur_min_mean_flow_time:
+            #        min_mean_flow_time_found = True
+            #        indiv.watch_individual = True
+            #        cur_flow_ind = indiv
+            #    else:
+            #        indiv.watch_individual = False
+            
+            #previous_makespan_min = cur_min_makespan
+            #previous_flow_min = cur_min_mean_flow_time
+
             if self.activate_logging:
                 # The method will act as generator if logging is active
                 yield cur_result

alg/NSGA_II/conf/experiment_1.yaml

@@ -9,6 +9,7 @@ classical:
         _target_: base_nsga_ii.ClassicalPopulation
         N: 50
         decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Completion Time"]
         activate_schedule: True
         tournament_size: 2
         mating_pool_size: 50
@@ -26,6 +27,7 @@ quantum_base_encoding:
         reset_fraction: 0.25 # Clip the bottom quarter -> reset chromosomes back to 50/50
         activate_schedule: True
         decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Completion Time"]
         individual_type: QChromosomeBaseEncoding
         time_log: False
         rotation_angles: "[0.02*np.pi, 0, 0.05*np.pi, 0, 0.05*np.pi, 0, 0.02*np.pi, 0]"
@@ -43,6 +45,7 @@ quantum_position_encoding:
         reset_fraction: 0.25 # Clip the bottom quarter -> reset chromosomes back to 50/50
         activate_schedule: True
         decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Completion Time"]
         individual_type: QChromosomePositionEncoding
         time_log: False
         rotation_angles: "[0.02*np.pi, 0, 0.05*np.pi, 0, 0.05*np.pi, 0, 0.02*np.pi, 0]"
@@ -62,6 +65,7 @@ quantum_position_encoding_restricted:
         reset_fraction: 0.25 # Clip the bottom quarter -> reset chromosomes back to 50/50
         activate_schedule: True
         decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Completion Time"]
         individual_type: QChromosomePositionEncoding
         time_log: False
         rotation_angles: "[0.02*np.pi, 0, 0.05*np.pi, 0, 0.05*np.pi, 0, 0.02*np.pi, 0]"
@@ -80,6 +84,7 @@ quantum_hash_base_encoding:
         reset_fraction: 0.25 # Clip the bottom quarter -> reset chromosomes back to 50/50
         activate_schedule: True
         decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Completion Time"]
         individual_type: QChromosomeHashBaseEncoding
         time_log: False
         rotation_angles: "[0.02*np.pi, 0, 0.05*np.pi, 0, 0.05*np.pi, 0, 0.02*np.pi, 0]"
@@ -98,6 +103,7 @@ quantum_hash_reduced_encoding:
         reset_fraction: 0.25 # Clip the bottom quarter -> reset chromosomes back to 50/50
         activate_schedule: True
         decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Completion Time"]
         individual_type: QChromosomeHashReducedEncoding
         time_log: False
         rotation_angles: "[0.02*np.pi, 0, 0.05*np.pi, 0, 0.05*np.pi, 0, 0.02*np.pi, 0]"

alg/NSGA_II/conf/experiment_1_base.yaml

@@ -16,6 +16,7 @@ quantum_base_encoding:
         reset_fraction: 0.25 # Clip the bottom quarter -> reset chromosomes back to 50/50
         activate_schedule: True
         decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Completion Time"]
         individual_type: QChromosomeRepairPermutationEncoding
         time_log: False
         rotation_angles: "[0.02*np.pi, 0, 0.05*np.pi, 0, 0.05*np.pi, 0, 0.02*np.pi, 0]"

alg/NSGA_II/conf/experiment_1_hash_base.yaml

@@ -18,6 +18,7 @@ quantum_hash_base_encoding:
         reset_fraction: 0 # Clip the bottom % -> reset chromosomes back to 50/50
         activate_schedule: True
         decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Completion Time"]
         individual_type: QChromosomeHashMultisetImprovedEncoding #QChromosomeHashMultisetEncoding
         time_log: False
         rotation_angles: "[0.1*np.pi, 0, 0.02*np.pi, 0, 0.02*np.pi, 0, 0.1*np.pi, 0]" # "[0.1*np.pi, 0, 0.05*np.pi, 0, 0.05*np.pi, 0, 0.1*np.pi, 0]"

alg/NSGA_II/conf/experiment_1_hash_restricted.yaml

@@ -17,6 +17,7 @@ quantum_hash_reduced_encoding:
         reset_fraction: 0.25 # Clip the bottom quarter -> reset chromosomes back to 50/50
         activate_schedule: True
         decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Completion Time"]
         individual_type: QChromosomeHashPermutationEncoding
         time_log: False
         rotation_angles: "[0.02*np.pi, 0, 0.05*np.pi, 0, 0.05*np.pi, 0, 0.02*np.pi, 0]"

alg/NSGA_II/conf/experiment_1_restricted_pos.yaml

@@ -19,6 +19,7 @@ quantum_position_encoding_restricted:
         reset_fraction: 0 # Clip the bottom quarter -> reset chromosomes back to 50/50
         activate_schedule: True
         decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Completion Time"]
         individual_type: QChromosomePositionEncoding
         time_log: False
         rotation_angles: "[0.02*np.pi, 0, 0.05*np.pi, 0, 0.05*np.pi, 0, 0.02*np.pi, 0]" #"[0.2*np.pi, 0, 0.02*np.pi, 0, 0.02*np.pi, 0, 0.2*np.pi, 0]" #"[0.02*np.pi, 0, 0.05*np.pi, 0, 0.05*np.pi, 0, 0.02*np.pi, 0]"

alg/NSGA_II/conf/experiment_2.yaml

@@ -14,6 +14,7 @@ classical:
         _target_: base_nsga_ii.ClassicalPopulation
         N: 50
         decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Completion Time"]
         activate_schedule: True
         tournament_size: 2
         mating_pool_size: 50
@@ -29,6 +30,7 @@ quantum_position_encoding:
         reset_fraction: 0 # Clip the bottom quarter -> reset chromosomes back to 50/50
         activate_schedule: True
         decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Completion Time"]
         individual_type: QChromosomePositionEncoding
         time_log: False
         rotation_angles: "[0.02*np.pi, 0, 0.05*np.pi, 0, 0.05*np.pi, 0, 0.02*np.pi, 0]" #"[0.2*np.pi, 0, 0.02*np.pi, 0, 0.02*np.pi, 0, 0.2*np.pi, 0]" #"[0.02*np.pi, 0, 0.05*np.pi, 0, 0.05*np.pi, 0, 0.02*np.pi, 0]"
@@ -47,6 +49,7 @@ quantum_hash_base_encoding:
         reset_fraction: 0 # Clip the bottom quarter -> reset chromosomes back to 50/50
         activate_schedule: True
         decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Completion Time"]
         individual_type: QChromosomeHashMultisetImprovedEncoding
         time_log: False
         rotation_angles: "[0.1*np.pi, 0, 0.02*np.pi, 0, 0.02*np.pi, 0, 0.1*np.pi, 0]"

alg/NSGA_II/conf/experiment_classical.yaml

@@ -4,7 +4,7 @@ defaults:
 experiment:
     repetitions: 10
     n_iterations: 150
-    problem_names: ["la21", "la22", "la23", "la24", "la25", "la26", "la27", "la29"] #["ft06", "ft10", "ft20", "abz7", "abz8", "abz9", "la28", "la40"]
+    problem_names: ["ft06", "ft10", "ft20", "abz7", "abz8", "abz9", "la28", "la40"] # ["la21", "la22", "la23", "la24", "la25", "la26", "la27", "la29"]
     dump_population: False
 
 classical:
@@ -15,6 +15,7 @@ classical:
         _target_: base_nsga_ii.ClassicalPopulation
         N: 50
         decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Flow Time"]
         activate_schedule: True
         tournament_size: 2
         mating_pool_size: 50

alg/NSGA_II/conf/experiment_classical_2.yaml

@@ -0,0 +1,23 @@
+defaults:
+- experiment: basic
+
+experiment:
+    repetitions: 10
+    n_iterations: 150
+    problem_names: ["la21", "la22", "la23", "la24", "la25", "la26", "la27", "la29"]
+    dump_population: False
+
+classical:
+    _target_: base_nsga_ii.ClassicalNSGAII
+    _partial_: True
+    activate_logging: True
+    pop_object:
+        _target_: base_nsga_ii.ClassicalPopulation
+        N: 50
+        decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Flow Time"]
+        activate_schedule: True
+        tournament_size: 2
+        mating_pool_size: 50
+        mutation_probability: 0.6
+    

alg/NSGA_II/conf/experiment_classical_test.yaml

@@ -4,7 +4,7 @@ defaults:
 experiment:
     repetitions: 1
     n_iterations: 150
-    problem_names: ['ft10']
+    problem_names: ['ft20']
 
 classical:
     _target_: base_nsga_ii.ClassicalNSGAII
@@ -14,8 +14,9 @@ classical:
         _target_: base_nsga_ii.ClassicalPopulation
         N: 50
         decoding_method: "apply_operation_based_bierwirth"
+        objectives: ["Makespan", "Mean Completion Time"]
         activate_schedule: True
         tournament_size: 2
-        mating_pool_size: 20
-        mutation_probability: 0.3
+        mating_pool_size: 50
+        mutation_probability: 0.8