matlab【2024年最新多目标优化算法】【多目标RIME算法】MORIME

clc; clear; close all; % Problem Configuration ObjectiveFunction = @ZDT1; % Objective function handle dim = 5; % Number of dimensions ub = ones(1, dim); % Upper bounds (assuming 1 for all dimensions) lb = zeros(1, dim); % Lower bounds (assuming 0 for all dimensions) obj_no = 2; % Number of objectives % Algorithm Parameters max_iter = 50; % Maximum number of iterations ArchiveMaxSize = 100; % Maximum size of the archive Archive_X = zeros(ArchiveMaxSize, dim); % Initialize archive solutions Archive_F = ones(ArchiveMaxSize, obj_no) * inf; % Initialize archive fitnesses Archive_member_no = 0; % Number of members in the archive Best_rime_rate = inf * ones(1, obj_no); % Best rate (fitness) initialization Best_rime = zeros(dim, 1); % Best solution initialization Rimepop = initialization(ArchiveMaxSize, dim, ub, lb); % Population initialization W = 5; % Weight factor in RIME algorithm (specific to MORIME) % Main loop for MORIME algorithm for iter = 1:max_iter % RimeFactor calculation based on iteration number RimeFactor = (rand - 0.5) * 2 * cos((pi * iter / (max_iter / 10))) * (1 - round(iter * W / max_iter) / W); E = sqrt(iter / max_iter); % Exploration factor % Update Rimepop (population) for i = 1:ArchiveMaxSize Rime_rates(i, :) = ObjectiveFunction(Rimepop(i, :)); for j = 1:dim r1 = rand(); if r1 < E % Update position based on Rime strategy Rimepop(i, j) = Best_rime(j) + RimeFactor * ((ub(j) - lb(j)) * rand + lb(j)); end normalized_rime_rates = normr(Rime_rates); r2 = rand(); if r2 < normalized_rime_rates(i) Rimepop(i, j) = Best_rime(j); % Correctly update Rimepop based on Best_rime end end % Boundary Check to keep solutions within bounds Flag4ub = Rimepop(i, :) > ub; Flag4lb = Rimepop(i, :) < lb; Rimepop(i, :) = (Rimepop(i, :) .* (~(Flag4ub + Flag4lb))) + ub .* Flag4ub + lb .* Flag4lb; end % Calculate Fitness for each individual in Rimepop Rime_rates = zeros(ArchiveMaxSize, obj_no); for i = 1:ArchiveMaxSize Rime_rates(i, :) = ObjectiveFunction(Rimepop(i, :)); end % Non-dominated Sorting and Crowding Distance Calculation Combined_X = [Rimepop; Archive_X(1:Archive_member_no, :)]; Combined_F = [Rime_rates; Archive_F(1:Archive_member_no, :)]; [fronts, ~] = NonDominatedSorting(Combined_F); crowdingDistances = CrowdingDistance(Combined_F, fronts); % Update Archive using NSGA-II strategies [Archive_X, Archive_F, Archive_member_no] = UpdateArchiveUsingNSGAII(fronts, crowdingDistances, Combined_X, Combined_F, ArchiveMaxSize); % Display iteration information disp(['At iteration ', num2str(iter), ', MORIME has ', num2str(Archive_member_no), ' non-dominated solutions in the archive']); end % Plotting the results figure; Draw_ZDT1(); % Function to draw the true Pareto Front (assuming it is defined) hold on; plot(Archive_F(:, 1), Archive_F(:, 2), 'ro', 'MarkerSize', 8, 'markerfacecolor', 'k'); legend('True PF', 'Obtained PF'); title('MORIME'); set(gcf, 'pos', [403 466 230 200]); % Setting the figure position and size