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: MTS.m
    path of file: ./repos/PlatEMO/PlatEMO/Algorithms/Multi-objective optimization/MTS
    
    the code of the file until where you have to start completion: classdef MTS < ALGORITHM
% <multi> <real/integer>
% Multiple trajectory search
% popsize           --- 40 --- Size of the population
% ofLocalSearchTest ---  5 --- Number of iterations for determining the best local search
% ofLocalSearch     --- 45 --- Number of iterations for applying the best local search
% ofForeground      ---  5 --- Number of best solutions for local search

%------------------------------- Reference --------------------------------
% L. Y. Tseng and C. Chen, Multiple trajectory search for unconstrained /
% constrained multi-objective optimization, Proceedings of the IEEE
% Congress on Evolutionary Computation, 2009, 1951-1958.
%------------------------------- 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".
%---------------------------------------------
    
function main(Algorithm,Problem)
            %% Parameter setting
            [popsize,ofLocalSearchTest,ofLocalSearch,ofForeground] = Algorithm.ParameterSet(40,5,45,5);

            %% Generate random population
            % Generate the SOA
            [~,SOA] = sort(rand(popsize,Problem.D));
            % Map the SOA to the decision space
            Decs = SOA./popsize.*repmat(Problem.upper-Problem.lower,popsize,1) + repmat(Problem.lower,popsize,1);
            % Generate the initial solutions
            Population  = Problem.Evaluation(Decs);
            AppSet      = Population;
            Enable      = true(1,popsize);
            Improve     = true(1,popsize);
            Grade       = zeros(1,popsize);
            SearchRange = repmat((Problem.upper-Problem.lower)/2,Problem.N,1);

            %% Optimization
            while Algorithm.NotTerminated(AppSet)
                for i = find(Enable)
                    Grade(i)     = 0;
                    LS_TestGrade = zeros(1,3);
                    for j = 1 : ofLocalSearchTest
                        [grade1,Population(i),SearchRange(i,:),Improve(i),AppSet] = LocalSearch1(Problem,Population(i),SearchRange(i,:),Improve(i),AppSet);
                        [grade2,Population(i),SearchRange(i,:),Improve(i),AppSet] = LocalSearch2(Problem,Population(i),SearchRange(i,:),Improve(i),AppSet);
                        [grade3,Population(i),SearchRange(i,:),Improve(i),AppSet] = LocalSearch3(Problem,Population(i),SearchRange(i,:),Improve(i),AppSet);
                        LS_TestGrade(1) = LS_TestGrade(1) + grade1;
                        LS_TestGrade(2) = LS_TestGrade(2) + grade2;
                        LS_TestGrade(3) = LS_TestGrade(3) + grade3;
                    end