长鼻浣熊优化算法如何应用于单目标优化问题求解？

本文共计489个文字，预计阅读时间需要2分钟。

“1+内容介绍+【智能优化算法-长鼻濑熊优化算法】基于长鼻濑熊优化算法求解单目标优化问题附Matlab代码+2+部分代码++Coati Optimization Algorithm：一种新的生物启发元启发式算法，用于解决优化问题

1 内容介绍

基于长鼻浣熊优化算法求解单目标优化问题附matlab代码

2 部分代码

%%

% Coati Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

% Knowledge-Based Systems

% Mohammad Dehghani, Zeinab Montazeri and Pavel Trojovský1

% Department of Mathematics, Faculty of Science, University of Hradec Králové, 50003 Hradec Králové, Czech Republic

% " Optimizer"

%%

clc

clear

close all

%%

%%

Fun_name='F4'; % number of test functions: 'F1' to 'F23'

SearchAgents=30; % number of Coati (population members)

Max_iterations=200; % maximum number of iteration

[lowerbound,upperbound,dimension,fitness]=fun_info(Fun_name); % Object function information

[Best_score,Best_pos,COA_curve]=COA(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,fitness); % Calculating the solution of the given problem using COA

%%

display(['The best solution obtained by COA for ' [num2str(Fun_name)],' is : ', num2str(Best_pos)]);

display(['The best optimal value of the objective funciton found by COA for ' [num2str(Fun_name)],' is : ', num2str(Best_score)]);

figure('Position',[284 214 660 290])

%Draw search space

subplot(1,2,1);

func_plot(Fun_name);

title('Test function')

xlabel('x_1');

ylabel('x_2');

zlabel([Fun_name,'( x_1 , x_2 )'])

grid off

%Draw objective space

subplot(1,2,2);

semilogy(COA_curve,'Color','b','linewidth',2)

title('Convergence curve')

xlabel('Iteration');

ylabel('Best flame (score) obtained so far');

axis tight

grid off

box on

legend('COA')

3 运行结果

4 参考文献

[1] Zeidabadi F A , Doumari S A , Dehghani M , et al. MLA:A New Mutated Leader Algorithm for Solving Optimization Problems[J]. 计算机、材料和连续体(英文), 2022(3):19.

部分理论引用网络文献，若有侵权联系博主删除。