人工大猩猩部队优化算法（GTO，ArtificialGorillaTroopsOptimizer）Matlab代码

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智能优化算法   神经网络预测 雷达通信 无线传感器

信号处理 图像处理 路径规划 元胞自动机 无人机

⛄ 内容介绍

This manuscript investigates the performance of newly developed soft computing algorithm called artificial gorilla troops optimizer (GTO) for determining optimal parameters of solar photovoltaic (PV) model. A very much used poly-crystalline PV module is mathematically modelled with single and double diode types for the analysis purpose. The parameters of the equivalent designed modeled have been derived with the suggested algorithm. For the accurate system performance characteristics, multiple points have been considered from the PV module data sheet in forming objective function. In this paper, the complete results have been demonstrated with S75 PV module practical data sheet standards.

⛄ 部分代码

%Artificial Gorilla Troops Optimizer

clear all

close all

clc

% Population size and stoppoing condition

pop_size=30;

max_iter=100;

% Define your objective function’s details here

Function_name=’F1’; % Name of the test function

[lower_bound,upper_bound,variables_no,fobj]=Get_Functions_details(Function_name);

[Silverback_Score,Silverback,convergence_curve]=GTO(pop_size,max_iter,lower_bound,upper_bound,variables_no,fobj);

figure(’Position’,[269   240   660   290])

% Best optimal values for the decision variables

subplot(1,2,1)

func_plot(Function_name);

xlabel(’Decision variables’)

ylabel(’Best estimated values ’)

box on

% Best convergence curve

subplot(1,2,2)

plot(convergence_curve,’Color’,’r’,’linewidth’,1.5)

title(’Convergence curve of GTO’)

xlabel(’Current_iteration’);

ylabel(’Objective value’);

box on

⛄ 运行结果

⛄ 参考文献

[1] Kumar V R ,  Bali S K ,  Devarapalli R . GTO Algorithm Based Solar Photovoltaic Module Parameter Selection[C]// 2021 Innovations in Power and Advanced Computing Technologies (i-PACT). 0.

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