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: TriMOEATAR.m
    path of file: ./repos/PlatEMO/PlatEMO/Algorithms/Multi-objective optimization/TriMOEA-TA&R
    
    the code of the file until where you have to start completion: classdef TriMOEATAR < ALGORITHM
% <multi> <real/integer> <multimodal>
% Multi-modal MOEA using two-archive and recombination strategies
% p_con       --- 0.5  --- Probability of selecting parents from the convergence archive
% sigma_niche --- 0.1  --- Niche radius in the decision space
% eps_peak    --- 0.01 --- Accuracy level to detect peaks
% NR          --- 100  --- Number of refernece points
% NCA         --- 20   --- Number of sampling solutions in control variable analysis
% NIA         --- 6    --- Maximum number of tries required to judge the interaction

%------------------------------- Reference --------------------------------
% Y. Liu, G. G. Yen, and D. Gong, A multi-modal multi-objective
% evolutionary algorithm using two-archive and recombination strategies,
% IEEE Transactions on Evolutionary Computation, 2019, 23(4):
    
function main(Algorithm,Problem)
            %% Parameter setting
            [p_con,sigma_niche,eps_peak,NR,NCA,NIA] = Algorithm.ParameterSet(0.5,0.1,0.01,100,20,6);  

            %% Decision Variable Analysis and Generate random population
            [Xic,Xre] = DecisionVariableAnalysis(Problem,NCA,NIA);

            %% Generate the reference points    
            R = UniformPoint(NR,Problem.M);

            %% Initialization
            Population = Problem.Initialization();
            NC = Problem.N;
            ND = Problem.N;
            AC = [];
            AD = [];
            Pc = floor(p_con.*Problem.N);
            Pd = Problem.N - Pc;

            %% Update Archives
            Z            = min(Population.objs,[],1);  
            [AC,RankC,~] = UpdateConvergenceArchive(AC,Population,NC,Z,Xic,sigma_niche,Problem);
            [AD,RankD]   = UpdateDiversityArchive(AD,Population,ND,R,Z,Xre,sigma_niche,Problem);

            %% Optimization
            while Algorithm.NotTerminated(AD)
                % Generate new Population based on two Archives
                MatingPoolC = TournamentSelection(2,Pc,RankC);
                MatingPoolD = TournamentSelection(2,Pd,RankD);
                Parents     = [AC(MatingPoolC),AD(MatingPoolD)];
                Parents     = Parents(randperm(Problem.N));
                Population  = OperatorGA(Problem,Parents);        
                % Update Archives
                Z             = min([Z;Population.objs],[],1);
                [AC,RankC,fS] = UpdateConvergenceArchive(AC,Population,NC,Z,Xic,sigma_niche,Problem);        
                [AD,RankD]    = UpdateDiversityArchive(AD,Population,ND,R,Z,Xre,sigma_niche,Problem);                
                % Recombination
                if Problem.FE >= Problem.maxFE && sum(Xic) > 0
                    FS = Recombination(AC,AD,Xic,Xre,eps_peak,fS,RankC);
                    AD = Problem.Evaluation(FS);
                end