function [Best_rime_rate, Best_pos, Convergence_curve, bestPred, bestNet, bestInfo] = RIME(SearchAgents_no, Max_iter, lb, ub, dim, fobj)初始化位置Best_rime = zeros(1, dim);Best_rime_rate = inf; % 用于最大化问题，请将此值改为 -inffor i = 1:dim:, i) = lb(i) + rand(SearchAgents_no, 1) .* (ub(i) - lb(i)); % 初始种群endLb = lb .* ones(1, dim); % 下边界Ub = ub .* ones(1, dim); % 上边界it = 1; % 迭代次数Convergence_curve = zeros(1, Max_iter);Rime_rates = zeros(1, SearchAgents_no); % 初始化适应度值newRime_rates = zeros(1, SearchAgents_no);W = 5; % 软霜冰参数，在论文第4.3.1节中有详细讨论计算初始位置的适应度值for i = 1:SearchAgents_noi),Value{i},Net{i},Info{i}] = fobj(Rimepop(i, :)); % 计算每个搜索体的适应度值进行贪婪选择if Rime_rates(1, i) < Best_rime_rateBest_rime_rate = Rime_rates(1, i);Best_rime = Rimepop(i, :);bestPred = Value{i};bestNet = Net{i};bestInfo = Info{i};endend主循环while it <= Max_iterRimeFactor = (rand - 0.5) * 2 * cos((pi * it / (Max_iter / 10))) * (1 - round(it * W / Max_iter) / W); % 公式(3),(4),(5)的参数E = (it / Max_iter)^0.5; % 公式(6)newRimepop = Rimepop; % 记录新的种群normalized_rime_rates = normr(Rime_rates); % 公式(7)的参数for i = 1:SearchAgents_nofor j = 1:dim软霜冰搜索策略r1 = rand();if r1 < Ej) = Best_rime(1, j) + RimeFactor * ((Ub(j) - Lb(j)) * rand + Lb(j)); % 公式(3)end硬霜冰穿刺机制r2 = rand();if r2 < normalized_rime_rates(i)j) = Best_rime(1, j); % 公式(7)endendendfor i = 1:SearchAgents_no边界吸收Flag4ub = newRimepop(i, :) > ub;Flag4lb = newRimepop(i, :) < lb;:) = (newRimepop(i, :) .* ~(Flag4ub + Flag4lb)) + ub .* Flag4ub + lb .* Flag4lb;i),newValue{i},newNet{i},newInfo{i}] = fobj(newRimepop(i, :));正向贪婪选择机制if newRime_rates(1, i) < Rime_rates(1, i)i) = newRime_rates(1, i);:) = newRimepop(i, :);= newValue{i};= newNet{i};= newInfo{i};
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完整代码：https://mbd.pub/o/bread/mbd-ZZealpds
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