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: DCNSGAIII.m
    path of file: ./repos/PlatEMO/PlatEMO/Algorithms/Multi-objective optimization/DCNSGA-III
    
    the code of the file until where you have to start completion: classdef DCNSGAIII < ALGORITHM
% <multi/many> <real/integer/label/binary/permutation> <constrained>
% Dynamic constrained NSGA-III
% cp --- 5 --- Decrease trend of the dynamic constraint boundary

%------------------------------- Reference --------------------------------
% R. Jiao, S. Zeng, C. Li, S. Yang, and Y. S. Ong, Handling constrained 
% many-objective optimization problems via problem transformation, IEEE 
% Transactions on Cybernetics, 2021, 51(10): 4834-4847.
%------------------------------- Copyright --------------------------------
% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for
% research pu
    
function is written by Ruwang Jiao
    
    methods
        function main(Algorithm,Problem)
            %% Parameter setting
            cp                    = Algorithm.ParameterSet(5);
            
            %% Generate the reference points and random population
            [Z,Problem.N]         = UniformPoint(Problem.N, Problem.M);
            Population            = Problem.Initialization();
            
            %% Calculate the initial dynamic constraint boundary
            [initialE, ~]         = max(max(0,Population.cons), [], 1);
            initialE(initialE==0) = 1;
            
            %% Optimization
            while Algorithm.NotTerminated(Population)
                % Reduce the dynamic constraint boundry
                epsn       = ReduceBoundary(initialE, ceil(Problem.FE/Problem.N), ceil(Problem.maxFE/Problem.N)-1, cp);
                % Mating selection which prefers to select epsilon-feasible solutions
                MatingPool = TournamentSelection(2, Problem.N,sum(max(0, Population.cons-epsn), 2));
                Offspring  = OperatorGA(Problem,Population(MatingPool));
                % Update the reference points only consider the epsilon-feasible solution set
                Zmin       = min([Population(sum(max(0, Population.cons)<=epsn, 2)==size(Population.cons, 2)).objs; Offspring(sum(max(0,Offspring.cons)<=epsn, 2)==size(Offspring.cons, 2)).objs], [], 1);
                % Environment selection
                Population = EnvironmentalSelection([Population, Offspring], Problem.N, Z, Zmin, initialE, epsn);
            end