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: CoMMEA.m
    path of file: ./repos/PlatEMO/PlatEMO/Algorithms/Multi-objective optimization/CoMMEA
    
    the code of the file until where you have to start completion: classdef CoMMEA < ALGORITHM
% <multi> <real/integer/label/binary/permutation> <multimodal>
% Coevolutionary multimodal multi-objective evolutionary algorithm
% eps --- 0.2 --- Parameter for quality of the local Pareto front (suggested to be 0 if the problem has no local Pareto front).

%------------------------------- Reference --------------------------------
% W. Li, X. Yao, K. Li, R. Wang, T. Zhang, and  L. Wang, Coevolutionary
% framework for generalized multimodal multi-objective optimization,
% IEEE/CAA Journal of Automatica Sinica, 2023, 10(7): 1544-1556.
%------------
    
function is written by Wenhua Li
% p.s. CoMMEA require more function evaluations to receive better
% performance for multimodal multiobjective optimization problems.
    
    methods
        function main(Algorithm,Problem)
            %% Parameter setting
            eps = Algorithm.ParameterSet(0.2);
            
           %% Generate random population
            Population1 = Problem.Initialization();
            Population2 = Problem.Initialization();
            Fitness1    = CalFitness(Population1,'Normal');
            Fitness2    = CalFitness(Population2,'LocalC');
            
           %% Optimization
            while Algorithm.NotTerminated(Population2)
                MatingPool1 = TournamentSelection(2,round(Problem.N/4),Fitness1);
                MatingPool2 = TournamentSelection(2,Problem.N,Fitness2);
                Offspring1  = OperatorGA(Problem,Population1(MatingPool1));
                Offspring2  = OperatorGA(Problem,Population2(MatingPool2));
                
               %% Enviornmental selection
                EvoState = Problem.FE / Problem.maxFE;
                CurEps = max(-log2(1.5*EvoState),eps); % Compute the current epsilon value
                [Population1,Fitness1] = EnvironmentalSelection1([Population1,Offspring1,Offspring2],Problem.N,'Normal',EvoState);
                [Population2,Fitness2] = EnvironmentalSelection2([Population2,Offspring1,Offspring2],Problem.N,'LocalC',CurEps);
            end