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: CMOQLMT.m
    path of file: ./repos/PlatEMO/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT
    
    the code of the file until where you have to start completion: classdef CMOQLMT < ALGORITHM
% <multi> <real> <constrained>
% Constrained multi-objective optimization based on Q-learning and multitasking
% delta --- 0.9 --- The probability of choosing parents locally
% nr    ---   2 --- Maximum number of solutions replaced by each offspring

%------------------------------- Reference --------------------------------
% F. Ming, W. Gong, and L. Gao, Adaptive auxiliary task selection for 
% multitasking-assisted constrained multi-objective optimization [feature],
% IEEE Computational Intelligence Magazine, 2023, 18(2): 18-30.
%------------------------------- 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 platform should acknowledge the use of "PlatEMO" and reference "Ye
% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform
% for evolutionary multi-objective optimization [educational forum], IEEE
% Computational Intelligence Magazine, 2017, 12(4): 73-87".
%--------------------------------------------------------------------------

% This function is written by Fei Ming

    methods
        function main(Algorithm,Problem)
            %% Generate random population
            Population{1} = Problem.Initialization();
            Population{2} = Problem.Initialization();
            Population{3} = Problem.Initialization();
          
    
function is written by Fei Ming

    methods
        function main(Algorithm,Problem)
            %% Generate random population
            Population{1} = Problem.Initialization();
            Population{2} = Problem.Initialization();
            Population{3} = Problem.Initialization();
            alpha1 = 2./(1+exp(1).^(-Problem.FE*10/Problem.maxFE))-1;
            para = ceil(Problem.maxFE/Problem.N)/2 - ceil(Problem.FE/Problem.N);
            Population{4} = Problem.Initialization();
            Zmin       = min(Population{4}.objs,[],1);
            Fmin       = min(Population{4}(all(Population{4}.cons<=0,2)).objs,[],1);
            Fitness{1} = CalFitness(Population{1}.objs,Population{1}.cons);
            Fitness{3} = CalFitness(Population{3}.objs);
            LengthO{1} = Problem.N/2; LengthO{2} = Problem.N/2; LengthO{3} = Problem.N/2; LengthO{4} = Problem.N/2;
            W          = UniformPoint(Problem.N,Problem.M);
            Ra         = 1;
            
            %% For QL
            num_task = 3;
            Q_Table = zeros(num_task,num_task);
            learning = 0.5;
            gama_ql = 0.9;
            alpha_ql = 0.8;
            greedy_ql = 0.9;
            current_state = randi(num_task);

            %% Optimization
            while Algorithm.NotTerminated(Population{1})
                if Problem.FE < learning * Problem.maxFE
                    % choose an action by greedy strategy based on Q table
                    if rand > greedy_ql || ( Q_Table(current_state,1) == Q_Table(current_state,2) && Q_Table(current_state,1) == Q_Table(current_state,3) )
                        action = randi(num_task);
                    else
                        [~,action] = max(Q_Table(current_state,:));
                    end