neighbourhood_selection_probability=0.9, max_number_of_replaced_solutions=2, weight_files_path='../../resources/MOEAD_weights', termination_criterion=StoppingByEvaluations(max=max_evaluations) ) algorithm.observable.register(observer=ProgressBarObserver(max=max_evaluations)) algorithm.observable.register( observer=VisualizerObserver(reference_front=problem.reference_front, display_frequency=1000)) algorithm.run() front = algorithm.get_result() # Plot front plot_front = Plot(plot_title='Pareto front approximation', reference_front=problem.reference_front, axis_labels=problem.obj_labels) plot_front.plot(front, label=algorithm.label, filename=algorithm.get_name()) # Plot interactive front plot_front = InteractivePlot(plot_title='Pareto front approximation', reference_front=problem.reference_front, axis_labels=problem.obj_labels) plot_front.plot(front, label=algorithm.label, filename=algorithm.get_name()) # Save results to file print_function_values_to_file(front, 'FUN.' + algorithm.label) print_variables_to_file(front, 'VAR.' + algorithm.label) print('Algorithm (continuous problem): ' + algorithm.get_name()) print('Problem: ' + problem.get_name()) print('Computing time: ' + str(algorithm.total_computing_time))
dimension=problem.number_of_objectives), neighbor_size=20, neighbourhood_selection_probability=0.9, max_number_of_replaced_solutions=2, weight_files_path='../../resources/MOEAD_weights', termination_criterion=StoppingByEvaluations(max=max_evaluations)) algorithm.observable.register(observer=ProgressBarObserver( max=max_evaluations)) algorithm.observable.register(observer=VisualizerObserver( reference_front=problem.reference_front, display_frequency=1000)) algorithm.run() front = algorithm.get_result() label = algorithm.get_name() + "." + problem.get_name() algorithm_name = label # Plot front plot_front = Plot(plot_title='Pareto front approximation', axis_labels=problem.obj_labels) plot_front.plot(front, label=label, filename=algorithm_name) # Plot interactive front plot_front = InteractivePlot(plot_title='Pareto front approximation', axis_labels=problem.obj_labels) plot_front.plot(front, label=label, filename=algorithm_name) # Save results to file print_function_values_to_file(front, 'FUN.' + label) print_variables_to_file(front, 'VAR.' + label)
algorithm.observable.register(observer=ProgressBarObserver( max=max_evaluations)) algorithm.observable.register(observer=VisualizerObserver( reference_front=problem.reference_front, display_frequency=1000)) algorithm.run() front = algorithm.get_result() # Plot front plot_front = Plot(plot_title='Pareto front approximation', reference_front=problem.reference_front, axis_labels=problem.obj_labels) plot_front.plot(front, label=algorithm.label, filename=algorithm.get_name()) # Plot interactive front plot_front = InteractivePlot(plot_title='Pareto front approximation', reference_front=problem.reference_front, axis_labels=problem.obj_labels) plot_front.plot(front, label=algorithm.label, filename=algorithm.get_name()) # Save results to file print_function_values_to_file(front, 'FUN.' + algorithm.label) print_variables_to_file(front, 'VAR.' + algorithm.label) print('Algorithm (continuous problem): ' + algorithm.get_name()) print('Problem: ' + problem.get_name())