You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: SMSEGO.m
    path of file: ./repos/PlatEMO/PlatEMO/Algorithms/Multi-objective optimization/SMS-EGO
    
    the code of the file until where you have to start completion: classdef SMSEGO < ALGORITHM
% <multi> <real/integer> <expensive>
% S-metric-selection-based efficient global optimization
% wmax --- 10000 --- The maximum number of internal evluation

%------------------------------- Reference --------------------------------
% W. Ponweiser, T. Wagner, D. Biermann, and M. Vincze, Multiobjective
% optimization on a limited budget of evaluations using model-assisted
% S-metric selection, Proceedings of the International Conference on
% Parallel Problem Solving from Nature, 2008, 784-794.
%------------------------------- Copyright --------------------------------
% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for
% research purpo
    
function is written by Cheng He

    methods
        function main(Algorithm,Problem)
            %% Parameter setting
            wmax = Algorithm.ParameterSet(10000);

            %% Generate initial population based on Latin hypercube sampling
            N          = 11*Problem.D-1;
            P          = UniformPoint(N,Problem.D,'Latin');
            Population = Problem.Evaluation(repmat(Problem.upper-Problem.lower,N,1).*P+repmat(Problem.lower,N,1));
            THETA      = 5.*ones(Problem.M,Problem.D);
            Model      = cell(1,Problem.M);
            % Lower confident bound parameter
            alpha = 1/normcdf(0.5+1/2^Problem.M);

            %% Optimization
            while Algorithm.NotTerminated(Population)
                % Delete duplicated solutions
                [~,index]  = unique(Population.decs,'rows');
                Population = Population(index);
                PopDec     = Population.decs;
                PopObj     = Population.objs;

                % Optimization
                for i = 1 : Problem.M
                    [dmodel,~] = dacefit(PopDec,PopObj(1:end