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冠豪猪优化算法(Crested Porcupine Optimizer,CPO)由Mohamed Abdel-Basset等人于2024年提出,该算法模拟冠豪猪的四种不同保护机制:视觉、听觉、气味和物理攻击。第一和第二防御技术(视觉和听觉)反映了CPO的探索行为,而第三和第四防御策略(气味和物理攻击)反映了CPO的剥削行为。该算法提出了一种称为循环种群减少技术的新策略,以模拟并非所有CP激活其防御机制,而只激活那些受到威胁的CP的介词。这种策略促进了趋同率和种群多样性。
[1]Abdel-Basset M, Mohamed R, Abouhawwash M. Crested Porcupine Optimizer: A new nature-inspired metaheuristic[J]. Knowledge-Based Systems, 2024, 284: 111257.
参考文献:
[1] Yao X, Liu Y, Lin G M. Evolutionary programming made faster[J]. IEEE transactions on evolutionary computation, 1999, 3(2):82-102.
close all ; clear clc Npop=30; Function_name='F1'; % Name of the test function that can be from F1 to F23 ( Tmax=500; [lb,ub,dim,fobj]=Get_Functions_details(Function_name); [Best_fit,Best_pos,Convergence_curve]=CPO(Npop,Tmax,lb,ub,dim,fobj); figure('Position',[100 100 660 290]) %Draw search space subplot(1,2,1); func_plot(Function_name); title('Parameter space') xlabel('x_1'); ylabel('x_2'); zlabel([Function_name,'( x_1 , x_2 )']) %Draw objective space subplot(1,2,2); semilogy(Convergence_curve,'Color','r','linewidth',3) title('Search space') xlabel('Iteration'); ylabel('Best score obtained so far'); axis tight grid on box on legend('CPO') saveas(gca,[Function_name '.jpg']); display(['The best solution is ', num2str(Best_pos)]); display(['The best fitness value is ', num2str(Best_fit)]);
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