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: MOMBIII.m
    path of file: ./repos/PlatEMO/PlatEMO/Algorithms/Multi-objective optimization/MOMBI-II
    
    the code of the file until where you have to start completion: classdef MOMBIII < ALGORITHM
% <multi/many> <real/integer/label/binary/permutation>
% Many objective metaheuristic based on the R2 indicator II
% alpha   ---   0.5 --- Threshold of variances
% epsilon --- 0.001 --- Tolerance threshold
% record  ---     5 --- The record size of nadir vectors

%------------------------------- Reference --------------------------------
% R. Hernandez Gomez and C. A. Coello Coello, Improved metaheuristic based
% on the R2 indicator for many-objective optimization, Proceedings of the
% Annual Conference on Genetic and Evolutionary Computation, 2015, 679-686.
%------------------------------- Copyright --------------------------------
% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for
% research purposes. All publications which use this platform or any code
% in the pla
    
function main(Algorithm,Problem)
            %% Parameter setting
            [alpha,epsilon,recordSize] = Algorithm.ParameterSet(0.5,0.001,5);

            %% Generate random population
            [W,Problem.N] = UniformPoint(Problem.N,Problem.M);
            Population    = Problem.Initialization();
            % Ideal and nadir points
            zmin = min(Population.objs,[],1);
            zmax = max(Population.objs,[],1);
            % For storing the nadir vectors of a few generations
            Record = repmat(zmax,recordSize,1);
            % For storing whether each objective has been marked for a few
            % generations
            Mark = false(recordSize,Problem.M);
            % R2 ranking procedure
            [Rank,Norm] = R2Ranking(Population.objs,W,zmin,zmax);

            %% Optimization
            while Algorithm.NotTerminated(Population)
                MatingPool  = TournamentSelection(2,Problem.N,Rank,Norm);
                Offspring   = OperatorGA(Problem,Population(MatingPool));
                Population  = [Population,Offspring];
                [Rank,Norm] = R2Ranking(Population.objs,W,zmin,zmax);
                [~,rank]    = sortrows([Rank,Norm]);
                Population  = Population(rank(1:Problem.N));
                Rank        = Rank(rank(1:Problem.N));
                Norm        = Norm(rank(1:Problem.N));
                [zmin,zmax,Record,Mark] = UpdateReferencePoints(Population.objs,zmin,zmax,Record,Mark,alpha,epsilon);
            end