1. Growth Optimization Algorithm GO
The Growth Optimizer (GO) was proposed by Qingke Zhang et al. in 2023. The design of the algorithm is inspired by the learning and reflection mechanism of individuals in the process of growth. Learning is a process in which individuals grow by acquiring knowledge from the outside world, and reflection is a process in which individuals check their own deficiencies, adjust individual learning strategies, and help individuals grow.
references:
Qingke Zhang, Hao Gao, Zhi-Hui Zhan, Junqing Li, Huaxiang Zhang ,Growth Optimizer: A powerful metaheuristic algorithm for solving continuous and discrete global optimization problems ,Knowledge-Based Systems,261,2023
The traveling salesman problem
3. The growth optimization algorithm GO solves the traveling salesman problem
%完整MATLAB code link: https://mbd.pub/o/bread/ZJiWmZZu
close all
clear
clc
%数据集参考文献 REINELT G.TSPLIB-a traveling salesman problem[J].ORSA Journal on Computing,1991,3(4):267-384.
global data
load('data.txt')%导入TSP数据集bayg29
Dim=size(data,1)-1;%维度
lb=-100;%下界
ub=100;%上界
fobj=@Fun;%计算总距离
SearchAgents_no=100; % 种群大小（可以修改）
Max_iteration=1000 partial results
The path obtained by the algorithm: 1 > 28 > 6 > 12 > 9 > 26 > 3 > 29 > 5 > 21 > 2 > 20 > 10 > 4 > 15 > 18 > 14 > 17 > 22 > 11 > 19 > 25 > 7 > 23 > 8 > 27 > 16 > 13 > 24 > 1
The total length of the total path solved by the algorithm: 9074.1
