【改进鲸鱼优化算法】增强型鲸鱼优化算法（Matlab代码实现）

xiaozongpeng · 发表于 2023-3-10 14:31

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1 概述

鲸鱼优化算法（WOA）是一个突出的问题解决器，广泛用于解决特征选择等 NP 难题。然而，它及其大多数变体都受到人口多样性低和搜索策略不佳的影响。为了减轻 WOA 的这些核心缺点，特别是为了处理功能选择问题，非常需要引入有效的策略。因此，本文致力于提出一种基于池化机制和2019种有效搜索策略的增强型鲸鱼优化算法E-WOA，分别是迁徙、优先选择和富集包围猎物。评估 E-WOA 的性能并与众所周知的 WOA 变体进行比较，以解决全局优化问题。获得的结果证明，E-WOA的性能优于WOA的变体。
2 运行结果

部分代码：
function [Convergence,Score_best,X_best]= E_WOA(SearchAgents_no,Max_iterproblem_size,lb,ub)
% Initialize the positions of search agents
Positions = initialization(SearchAgents_no,problem_size,ub,lb);
for j = 1:SearchAgents_no
Fitness(j,1)  = Function(Positions(j,:));
end
% Set the best position for leader
[temp_fit, sorted_index] = sort(Fitness, &#39;ascend&#39;);
X_best = Positions(sorted_index(1),:);
Score_best = temp_fit(1);
% ==================================================== %
P_rate =  20;                   % The portion rate
Pool.Kappa = 1.5 * SearchAgents_no; % The maximum pool size
Pool.position = zeros(0, problem_size);  % The solutions stored in the pool
Idx0 = (SearchAgents_no - (Pool.Kappa * 0.3)+1);
X_worst = Positions(sorted_index(Idx0: end),:);
Pool = Pooling_Mechanism(Pool,problem_size,X_worst, X_best);

Convergence = zeros(1,Max_iter);
t = 1;
% Main loop
while t <= Max_iter

a2 = -1+t*((-1)/Max_iter);

% Randomly select a P portion of the humpback whales
P_portion = randperm(SearchAgents_no,P_rate);

Pop_Pool = Pool.position;
[P_rnd1, P_rnd2] = RandIndex(size(Pop_Pool,1),  SearchAgents_no);

%% Probability rate
p = rand(SearchAgents_no, 1);

%% Cauchy distribution
A = 0.5 + 0.1 * tan(pi * (rand(SearchAgents_no, 1) - 0.5));
Idx = find(A <= 0);
while ~ isempty(Idx)
      A(Idx) = 0.5 + 0.1 * tan(pi * (rand(length(Idx), 1) - 0.5));
      Idx = find(A <= 0);
end
A = min(A, 1);

for i = 1:size(Positions,1)
      if (i ~= P_portion)
         C = 2*rand;    % Eq. (6) in the paper
         b = 1;
         for j=1:size(Positions,2)
            l=(a2-1)*rand+1;
            if p(i) < 0.5
                  if A(i) < 0.5    % Enriched encircling prey search strategy
                     rand_leader_index = floor(size(Pop_Pool,1)*rand()+1); % Randomly selected from the matrix Pool
                     P_rnd3 = Pop_Pool(rand_leader_index, j);
                     D_prim = abs(C * X_best(j) - P_rnd3);  % Eq. (17)
                     X(i,j) =  X_best(j)- A(i)*D_prim;    % Eq. (16)

                  elseif A(i)>= 0.5 % Preferential selecting search strategy
                     X(i,j) = Positions(i,j) + A(i)*( C* Pop_Pool(P_rnd1(i),j)- Pop_Pool(P_rnd2(i),j)) ; % Eq. (15)
                  end
            elseif p(i)>=0.5    % Spiral bubble-net attacking
                  D_prim =  abs(X_best(j)- Positions(i,j));          % Eq. (10)
                  X(i,j)=  D_prim * exp(b.*l).*cos(2*pi*l)+ X_best(j);% Eq. (9)
            end
         end
      end
end
%% Migrating search strategy
best_max = max(X_best);
best_min = min(X_best);
P_portion = P_portion&#39;;
X_rnd  =  rand(size(P_portion,1),problem_size).* (ub - lb) + lb;  % Eq.(13)
X_brnd =  rand(size(P_portion,1),problem_size).*(best_max - best_min)+ best_min  ; % Eq.(14)
X(P_portion, :)  = X_rnd -  X_brnd; % Eq.(12)

%% Computing the fitness of whales
for i = 1: size(Positions,1)
      X(i,:) = boundConstraint(X(i,:), Positions(i,:), lb,ub);
      Fit(i,1) = Function(X(i,:));
      if Fit(i,1) < Score_best
         Score_best =  Fit(i,1);
         X_best =  X(i,:);
      end
end
I = (Fitness > Fit);
Ind = find(I == 1);
X_worst = Positions(Ind, :);
% Definition 1. (Pooling mechanism)
Pool = Pooling_Mechanism(Pool, problem_size, X_worst, X_best);

% Updating the position of whales
Fitness(Ind) = Fit(Ind) ;
Positions(Ind, :) = X(Ind, :);

Convergence(t) = Score_best;
t = t + 1;
end
end
3 参考文献

部分理论来源于网络，如有侵权请联系删除。

Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study - ScienceDirect
4 Matlab代码实现

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【改进鲸鱼优化算法】增强型鲸鱼优化算法（Matlab代码实现）

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