Parallelization with MOEAD

[Kookaburra13]

Hi ! Thanks for the package, it is really helpful and easy to use.

I have a problem with 19 variables and 3 objectives, NSGA3 works perfectly, and I wanted to compare with MOEAD. However, the parallelization doesn't work when replacing the algorithm by MOEAD (and adding some arguments such as n_neighbors and prob_neighbor_mating). Is it specific to MOEAD ?

Here is the code for NSGA3. I tried MOEAD and ParallelMOEAD instead of NSGA3, but parallelization doesn't work for both.

class MyProblem(Problem):
    def __init__(self, **kwargs):
        super().__init__(n_var=19, n_obj=3, elementwise_evaluation=True,
                         xl=np.array([0.16, 0.0006, 500, 0.0144, 9, 0.50, 0.5, 0.065, 0.94, 0.008,
                                      0.72, 0, 1.26, 0.04, 20, 0.0, 0.3, 0.0, 0.0]),
                         xu=np.array([0.5, 0.002, 3500, 0.0216, 18, 0.57, 0.9, 0.075, 0.99, 0.02,
                                      0.95, 5, 1.54, 0.135, 50, 1, 0.5, 1.0, 1.0]))
        self.Variete = 'Skyscraper'
        self.names = ['alfa', 'Xn', 'Do_Leun', 'SpLAI', 'm_Leun', 'A_brunt', 'a', 'B_brunt',
                      'epsilon_leaf', 'b_Leun', 'sigma_NIR', 'PenPar2', 'NNI_crit',
                      'K_sat_1', 'ShldResC', 'delta_Leun', 'k_net', 'SOrgPhotEff', 'StemPhotEff']
        # Getting measurements
        self.MeasWSOrg, self.MeasNEE, self.MeasLE, self.ObsDate = funcs.measured_data(self.Variete)

    def _evaluate(self, x, out):
        with ThreadPool(23) as pool:
            # Getting model ouputs when there are measurements and rRMSE computation
            def run_with_id(idx, param_values):
                return funcs.running_daisy(param_names=self.names, ObsDate=self.ObsDate,
                                           Variete=self.Variete, MeasWSOrg=self.MeasWSOrg,
                                           MeasNEE=self.MeasNEE, MeasLE=self.MeasLE,
                                           x=param_values[0], thread_id=idx)
            params = [[x[k]] for k in range(len(x))]
            inputs_with_ids = [(idx, param) for idx, param in enumerate(params)]
            outputs = pool.starmap(run_with_id, inputs_with_ids)
            out["F"] = np.array(outputs)
if __name__ == "__main__":
    problem = MyProblem()
    initial_X = np.array(pd.read_csv('pop.csv').iloc[:, 1:])

    ref_dirs = get_reference_directions("energy", 3, 200, seed=1)
    algorithm = NSGA3(ref_dirs=ref_dirs, elminate_duplicates=True, sampling=initial_X,
                      crossover=SBX(prob=0.8, eta=30), mutation=PM(eta=20))

    res = minimize(problem, algorithm, termination=('n_gen', 1000), seed=1,
                   verbose=True)
    print('Processes:', res.exec_time)

Thanks !

[blankjul]

Can you please post some source code to reproduce your error?
(please use a test problem and avoid any imports that are not from standard libraries)