【优化求解】基于利希滕贝格优化LOA算法求解单目标问题matlab源码.pdf
需积分: 5 120 浏览量
2021-09-24
13:00:21
上传
评论
收藏 116KB PDF 举报
【优化求解】基于利希滕贝格优化算法求解单目标问题matlab源
码
1 算法介绍
Optimization is an essential tool to minimize or maximize functions, obtaining optimal results on costs,
mass, energy, gains, among others. Actual problems may be multimodal, nonlinear, and discontinuous
and may not be minimized by classical analytical methods that depend on the gradient. In this context,
there are metaheuristic algorithms inspired by natural phenomena to optimize real engineering problems.
No algorithm is the worst or the best, but more efficient for a given problem. Thus, a new nature-inspired
algorithm called Lichtenberg Optimization Algorithm (LA) is applied in this study to solve a complex
inverse damage identification problem in mechanical structures built by composite material. To verify the
performance of the new algorithm, both LA and Finite Element Method (FEM) were used to identify
delamination damage and the results were compared to other algorithms such as Genetic Algorithm (GA)
and SunFlower Optimization (SFO). LA was shown to be a powerful damage identification tool since it
was able to detect damage even in particular situations like noisy response and low damage severity.
2 部分代码
% LICHTENBERG ALGORITHM (LA) FOR NONLINEAR UNCONSTRAINED
% OPTIMIZATION
%
%
% Copyright (c) 2020, Jo茫o Luiz Pereira and Guilherme Ferreira Gomes
% All rights reserved.
%
%
% Please cite this algorithm as:
%
% Pereira, J.L.J., Chuman, M., Cunha Jr, S.S. and Gomes, G.F. (2020),
% "Lichtenberg optimization algorithm applied to crack tip identification
% in thin plate-like structures", Engineering Computations,
%% Initialization
clear all
close all
clc
format long
tic
set(0,'DefaultAxesFontName', 'Times New Roman')
set(0,'DefaultAxesFontSize', 14)
set(0,'DefaultTextFontname', 'Times New Roman')
set(0,'DefaultTextFontSize', 26)
% Bounds
UB = [5 5]; %upper bounds
LB = [-5 -5]; %lower bounds
d = length(LB); %problem dimension
% LA Parameters
pop = 20; %Population size
matlab科研社
- 粉丝: 1w+
- 资源: 100
评论0