CEC2005: Tasmanian devil optimization algorithm TDO solves CEC2005 (2005 IEEE Conference on Evolutionary Computation) provides MATLAB code

1. Tasmanian Devil Optimization Algorithm TDO

1.1 Algorithm Introduction

Tasmanian Devil Optimization (TDO) was proposed by MOHAMMAD DEHGHANI et al. in 2022. This algorithm simulates the eating behavior of Tasmanian Devils in nature. The idea of ​​TDO is novel, efficient and quick.

Tasmanian devils are stocky and stocky, with a large head and short tail. The fat-stored tail is an indicator of a devil's health, as a thin tail indicates poor health. Tasmanian devils have black fur, but often have small white patches on the chest and rump. The body length is 525-800 mm, the tail length is 230-300 mm, and the weight is 4.1-11.8 kg. The coat color is dark brown or gray, with white patches on the throat and rump, and the kiss is light pink. The body shape is similar to mustelids. There is a pouch in the abdomen. Tasmanian devils differ from other marsupials in that their front feet are slightly longer than their hind feet. They can run at a top speed of 13 km/h (8.1 mph). Male Tasmanian devils are generally larger than females: the average body length of males is 652 mm (with a tail length of 258 mm), and an average weight of 8 kg; the average body length of females is 570 mm (with a tail length of 244 mm), and an average weight of 6 kg . Wild Tasmanian devils live up to 6 years, but captive devils live longer.

1.2 Algorithm principle

参考文献：M. Dehghani, Š. Hubálovský and P. Trojovský, “Tasmanian Devil Optimization: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm,” in IEEE Access, vol. 10, pp. 19599-19620, 2022, doi: 10.1109/ACCESS.2022.3151641.

The Tasmanian devil optimization algorithm simulates the feeding behavior of Tasmanian devils, one is to rely on carrion, and the other is to rely on hunting. In Tasmanian devil foraging, if carrion is present, Tasmanian devils will eat carrion, otherwise Tasmanian devils will look for prey and hunt for food.

1.2.1 Strategy 1: Feed on carrion (global exploration stage)

This stage simulates that the Tasmanian Devil prefers to eat carrion instead of hunting, and its position is updated as follows:

1.2.2 Strategy 2: Hunting for food (local mining stage)

This phase is divided into finding prey and attacking prey:

(1) The location of Tasmanian devils in the hunting stage is updated as follows:

(2) The position of the Tasmanian devil in the attacking prey stage is updated as follows:

1.3 Algorithm process

参考文献：
M. Dehghani, Š. Hubálovský and P. Trojovský, “Tasmanian Devil Optimization: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm,” in IEEE Access, vol. 10, pp. 19599-19620, 2022,
doi: 10.1109/ACCESS.2022.3151641.

2. Brief introduction of CEC2005 test

The CEC2005 test set contains a total of 25 test questions, namely $f_1-f_{25}$. According to the characteristics of the problem, it can be further divided into four categories: unimodal problem $f_1-f_5$, the basic multimodal problem $f_6-f_{12}$, extended multimodal problem $f_{13}-f_{14}$and mixed compound problem $f_{15}-f_{25}$. In particular, the unimodal problem $f_1-f_5$The structure of is relatively simple, and is often used to test the performance of the algorithm; while the basic multimodal problem $f_6-f_{12}$There are multiple local optimal solutions, which are used to test the algorithm’s ability to balance the global exploration and local development of the decision space; extended multimodal and mixed composite problems $f_{13}-f_{25}$With a more complex structure, it is more challenging for the algorithm. In addition, the test set contains eight sets of comparable problems, which can test the performance of the algorithm on different types of problem characteristics, such as different condition numbers, rotations, noise interference, discontinuity, global optimal neighborhood shape, and global optimal solution. The position and the initial range of the population, etc. It can be seen that the test set can effectively evaluate the performance of the algorithm.

References:
[1] Suganthan PN , Hansen N , Liang JJ , et al. Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization[J]. Natural Computing, 2005, 341-357. [2
] Tian Mengnan. Research on Efficient Differential Evolution Algorithm for Global Optimization Problems [D]. Shaanxi Normal University, 2020.

3. Experimental results

The complete code is added to the blogger WeChat at the bottom of the blog: djpcNLP123

close all
clear 
clc
addpath(genpath(pwd));%加载路径
SearchAgents_no=100; % 种群大小
Function_name=5; %测试函数1-25
Max_iteration=50; % 最大迭代次数
dim=2;%维度 2/10/30/50
Xmin=[-100,-100,-100,-100,-100,-100,0,-32,-5,-5,-0.5,-pi,-3,-100,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,2];
Xmax=[100,100,100,100,100,100,600,32,5,5,0.5,pi,1,100,5,5,5,5,5,5,5,5,5,5,5];
lb=Xmin(Function_name);%变量下界
ub=Xmax(Function_name);%变量上界
[fMin,bestX,curve]=TDO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);  
figure
plot(curve,'Color','r','linewidth',2.5)
title(['F' num2str(Function_name)])
xlabel('Iteration');
ylabel('BestScore');
grid on
legend('TDO')
% function_plot(Function_name)%画函数图像
display(['The best solution obtained  is : ', num2str(bestX)]);
display(['The best optimal value of the objective funciton found  is : ', num2str(fMin)]);

F5 solution process of cec2005

F24 solution process of cec2005

Some experimental results:

3.1F11

3.2F12

3.3F13

3.4F14

3.5F15

4. Reference code

The complete code is added to the blogger WeChat at the bottom of the blog: djpcNLP123