Using Borg in Parallel and Serial with a Python Wrapper – Part 2

This blog post is Part 2 of a two-part series that will demonstrate how I have coupled a pure Python simulation model with the Borg multi-objective evolutionary algorithm (MOEA). I recommend reading Part 1 of this series before you read Part 2. In Part 1, I explain how to get Borg and provide sample code showing how you can access Borg’s serial and/or parallelized (master-slave) implementations through a Python wrapper ( In Part 2, I provide details for more advanced simulation-optimization setups that require you pass additional information from the borg wrapper into the simulation model (the “function evaluation”) other than just decision variable values.

In Part 1, the example simulation model I use (PySedSim) is called through a function handle “Simulation_Caller” in the file. Borg needs only this function handle to properly call the simulation model in each “function evaluation”. Borg’s only interaction with the simulation model is to pass the simulation model’s function handle (e.g., “Simulation_Caller”) the decision variables, and nothing else. In many circumstances, this is all you need.

However, as your simulation-optimization setup becomes more complex, in order for your simulation model (i.e., the function evaluation) to execute properly, you may need to pass additional arguments to the simulation model from Borg other than just the decision variables. For example, in my own use of Borg in a simulation-optimization setting, in order to do optimization I first import a variety of assumptions and preferences to set up a Borg-PySedSim run. Some of those assumptions and preferences are helpful to the simulation model (PySedSim) in determining how to make use of the decision variable values Borg feeds it. So, I would like to pass those relevant assumptions and preferences directly into the Borg wrapper (, so the wrapper can in turn pass them directly into the simulation model along with the decision variable values.

Before I show how to do this, let me provide a more concrete example of how/why I am doing this in my own research. In my current work, decision variable values represent parameters for a reservoir operating policy that is being optimized. The simulation model needs to know how to take the decision variable values and turn them into a useful operating policy that can be simulated. Some of this information gets imported in order to run Borg, so I might as well pass that information directly into the simulation model while I have it on hand, rather than importing it once again in the simulation model.

To do what I describe above, we just need to modify the two functions in the module so that a new argument “additional_inputs” is passed from borg to the simulation handle.  Using my python code from blog post 1, I provide code below that is modified in the Simulation_Caller() function on lines 5, 21, 22 and 27; and in the Optimization() function on lines 55, 56 and 70. After that code, I then indicate how I modify the wrapper so it can accept this information.

import numpy as np
import pysedsim # This is your simulation model
import platform  # helps identify directory locations on different types of OS

def Simulation_Caller(vars, additional_inputs):
    Purpose: Borg calls this function to run the simulation model and return multi-objective performance.

    Note: You could also just put your simulation/function evaluation code here.

        vars: A list of decision variable values from Borg
        additional_inputs: A list of python data structures you want to pass from Borg into the simulation model.
        performance: policy's simulated objective values. A list of objective values, one value each of the objectives.

    borg_vars = vars  # Decision variable values from Borg

    # Unpack lists of additional inputs from Borg (example assumes additional inputs is a python list with two items)
    borg_dict_1 = additional_inputs[0]
    borg_dict_2 = additional_inputs[1]

    # Reformat decision variable values as necessary (.e.g., cast borg output parameters as array for use in simulation)
    op_policy_params = np.asarray(borg_vars)
    # Call/run simulation model with decision vars and additional relevant inputs, return multi-objective performance:
    performance = pysedsim.PySedSim(decision_vars = op_policy_params, sim_addl_input_1 = borg_dict_1, sim_addl_input_2 = borg_dict_2)
    return performance

def Optimization():


    Purpose: Call this method from command line to initiate simulation-optimization experiment

        --pareto approximate set file (.set) for each random seed
        --Borg runtime file (.runtime) for each random seed


    import borg as bg  # Import borg wrapper

    parallel = 1  # 1= master-slave (parallel), 0=serial

    # The following are just examples of relevant MOEA specifications. Select your own values.
    nSeeds = 25  # Number of random seeds (Borg MOEA)
    num_dec_vars = 10  # Number of decision variables
    n_objs = 6  # Number of objectives
    n_constrs = 0  # Number of constraints
    num_func_evals = 30000  # Number of total simulations to run per random seed. Each simulation may be a monte carlo.
    runtime_freq = 1000  # Interval at which to print runtime details for each random seed
    decision_var_range = [[0, 1], [4, 6], [-1,4], [1,2], [0,1], [0,1], [0,1], [0,1], [0,1], [0,1]]
    epsilon_list = [50000, 1000, 0.025, 10, 13, 4]  # Borg epsilon values for each objective
    borg_dict_1 = {'simulation_preferences_1': [1,2]}  # reflects data you want Borg to pass to simulation model
    borg_dict_2 = {'simulation_preferences_2': [3,4]}  # reflects data you want Borg to pass to simulation model

    # Where to save seed and runtime files
    main_output_file_dir = 'E:\output_directory'  # Specify location of output files for different seeds
    os_fold = Op_Sys_Folder_Operator()  # Folder operator for operating system
    output_location = main_output_file_dir + os_fold + 'sets'

    # If using master-slave, start MPI. Only do once.
    if parallel == 1:
        bg.Configuration.startMPI()  # start parallelization with MPI

    # Loop through seeds, calling borg.solve (serial) or borg.solveMPI (parallel) each time
    for j in range(nSeeds):
        # Instantiate borg class, then set bounds, epsilon values, and file output locations
        borg = bg.Borg(num_dec_vars, n_objs, n_constrs, Simulation_Caller, add_sim_inputs = [borg_dict_1, borg_dict_2])
        borg.setBounds(*decision_var_range)  # Set decision variable bounds
        borg.setEpsilons(*epsilon_list)  # Set epsilon values
        # Runtime file path for each seed:
        runtime_filename = main_output_file_dir + os_fold + 'runtime_file_seed_' + str(j+1) + '.runtime'
        if parallel == 1:
            # Run parallel Borg
            result = borg.solveMPI(maxEvaluations='num_func_evals', runtime=runtime_filename, frequency=runtime_freq)

        if parallel == 0:
            # Run serial Borg
            result = borg.solve({"maxEvaluations": num_func_evals, "runtimeformat": 'borg', "frequency": runtime_freq,
                                 "runtimefile": runtime_filename})

        if result:
            # This particular seed is now finished being run in parallel. The result will only be returned from
            # one node in case running Master-Slave Borg.

            # Create/write objective values and decision variable values to files in folder "sets", 1 file per seed.
            f = open(output_location + os_fold + 'Borg_DPS_PySedSim' + str(j+1) + '.set', 'w')
            f.write('#Borg Optimization Results\n')
            f.write('#First ' + str(num_dec_vars) + ' are the decision variables, ' + 'last ' + str(n_objs) +
                    ' are the ' + 'objective values\n')
            for solution in result:
                line = ''
                for i in range(len(solution.getVariables())):
                    line = line + (str(solution.getVariables()[i])) + ' '

                for i in range(len(solution.getObjectives())):
                    line = line + (str(solution.getObjectives()[i])) + ' '


            # Create/write only objective values to files in folder "sets", 1 file per seed. Purpose is so that
            # the file can be processed in MOEAFramework, where performance metrics may be evaluated across seeds.
            f2 = open(output_location + os_fold + 'Borg_DPS_PySedSim_no_vars' + str(j+1) + '.set', 'w')
            for solution in result:
                line = ''
                for i in range(len(solution.getObjectives())):
                    line = line + (str(solution.getObjectives()[i])) + ' '


            print("Seed %s complete") %j

    if parallel == 1:
        bg.Configuration.stopMPI()  # stop parallel function evaluation process

def Op_Sys_Folder_Operator():
    Function to determine whether operating system is (1) Windows, or (2) Linux

    Returns folder operator for use in specifying directories (file locations) for reading/writing data pre- and

    if platform.system() == 'Windows':
        os_fold_op = '\\'
    elif platform.system() == 'Linux':
        os_fold_op = '/'
        os_fold_op = '/'  # Assume unix OS if it can't be identified

    return os_fold_op


Next, you will need to acquire the Borg wrapper using the instructions I specified in my previous blog post. You will need to make only two modifications: (1) modify the Borg class in so it accepts the inputs you want to pass to the simulation; and (2) some additional internal accounting in to ensure those inputs are passed to the methods that deal with your function handle. I will address these two in order.

First, modify the Borg class in so it now accepts an additional input (I only show some of the code here, just to indicate where changes are being made):

class Borg:
    def __init__(self, numberOfVariables, numberOfObjectives, numberOfConstraints, function, epsilons = None, bounds = None, directions = None, add_sim_inputs=None):

    # add_sim_inputs is the new input you will pass to borg


Then, modify the portion of the wrapper where self.function is called, so it can accommodate any simulation inputs you have specified.

if add_pysedsim_inputs is None:
    self.function = _functionWrapper(function, numberOfVariables, numberOfObjectives, numberOfConstraints, directions)
    # More simulation inputs are specified and can be passed to the simulation handle
    self.function = _functionWrapper(function, numberOfVariables, numberOfObjectives, numberOfConstraints, directions, addl_inputs=add_sim_inputs)

After the above, the last step is to modify the _functionWrapper method in

def _functionWrapper(function, numberOfVariables, numberOfObjectives, numberOfConstraints, directions=None, addl_inputs=None):
    # addl_inputs will be passed into the simulation model
    def innerFunction(v,o,c):
    global terminate
        if addl_inputs is None:
            result = function(*[v[i] for i in range(numberOfVariables)])
            result = function([v[i] for i in range(numberOfVariables)], addl_inputs)

2 thoughts on “Using Borg in Parallel and Serial with a Python Wrapper – Part 2

  1. Pingback: Water Programming Blog Guide (Part I) – Water Programming: A Collaborative Research Blog

  2. Any idea on how to set the runtime file to print values after a given number of function evaluations? I am using the Master-Slave implementation of BORG. When I try the ‘frequency’ argument in the function call, as described in this post, e.g. borg.solveMPI(maxEvaluations, maxTime, runtime, frequency), I receive an error that ‘frequency’ is an unknown argument. Right now, the runtime file prints every 100 NFEs thus generating very large files and I want to prevent this. Thank you.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s