1. Introduction
1 Application background of the
simulated annealing algorithm The simulated annealing algorithm was proposed in 1982. Kirkpatrick et al. first realized the similarity between the solid annealing process and the optimization problem; Metropolis et al.'s simulation of the process of solids reaching thermal equilibrium at a constant temperature also gave them enlightenment. By introducing the Metropolis algorithm into the optimization process, an iterative optimization algorithm for the Metropolis algorithm is finally obtained. This algorithm is similar to the solid annealing process and is called the "simulated annealing algorithm".
The simulated annealing algorithm is a random search algorithm suitable for solving large-scale combinatorial optimization problems. At present, the simulated annealing algorithm has achieved satisfactory results in solving combination optimization problems such as TSP and VLSI circuit design. Combining simulated annealing algorithms with other computational intelligence methods and applying them to modeling and optimization problems of various complex systems has also received more and more attention, and has gradually become an important development direction.
2 Introduction to Simulated Annealing Algorithm 
3 Simulated Annealing Algorithm's Parameters
Simulated Annealing is an optimization algorithm. It cannot exist independently. It needs an application. Temperature is the parameter to be optimized for simulated annealing. If it is applied to cluster analysis In the cluster analysis, there are certain or several parameters that need to be optimized, and this parameter or parameter set is represented by temperature. It can be a certain index, a certain degree of relevance, a certain distance, and so on.
Second, the source code
% clc;
close all;clear all;
p=[ 1739.94 1675.15 2395.96
373.3 3087.05 2429.47
1756.77 1652 1514.98
864.45 1647.31 2665.9
222.85 3059.54 2002.33
877.88 2031.66 3071.18
1803.58 1583.12 2163.05
2352.12 2557.04 1411.53
401.3 3259.94 2150.98
363.34 3477.95 2462.86
1571.17 1731.04 1735.33
104.8 3389.83 2421.83
499.85 3305.75 2196.22
2297.28 3340.14 535.62
2092.62 3177.21 584.32
1418.79 1775.89 2772.9
1845.59 1918.81 2226.49
2205.36 3243.74 1202.69
2949.16 3244.44 662.42
1692.62 1867.5 2108.97
1680.67 1575.78 1725.1
2802.88 3017.11 1984.98
172.78 3084.49 2328.65
2063.54 3199.76 1257.21
1449.58 1641.58 3405.12
1651.52 1713.28 1570.38
341.59 3076.62 2438.63
291.02 3095.68 2088.95
237.63 3077.78 2251.96
1702.8 1639.79 2068.74
1877.93 1860.96 1975.3
867.81 2334.68 2535.1
1831.49 1713.11 1604.68
460.69 3274.77 2172.99
2374.98 3346.98 975.31
2271.89 3482.97 946.7
1783.64 1597.99 2261.31
198.83 3250.45 2445.08
1494.63 2072.59 2550.51
1597.03 1921.52 2126.76
1598.93 1921.08 1623.33
1243.13 1814.07 3441.07
2336.31 2640.26 1599.63
354 3300.12 2373.61
2144.47 2501.62 591.51
426.31 3105.29 2057.8
1507.13 1556.89 1954.51
343.07 3271.72 2036.94
2201.94 3196.22 935.53
2232.43 3077.87 1298.87
1580.1 1752.07 2463.04
1962.4 1594.97 1835.95
1495.18 1957.44 3498.02
1125.17 1594.39 2937.73
24.22 3447.31 2145.01
1269.07 1910.72 2701.97
1802.07 1725.81 1966.35
1817.36 1927.4 2328.79
1860.45 1782.88 1875.13
];
[num,n]=size(p); %样品数目
centernum=4; %类别数目
IDXO=[1 2 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ];
% size(IDXO)
CO(1,:)=[ 1739.94 1675.15 2395.96];
CO(2,:)=[373.3 3087.05 2429.47];
CO(3,:)=[1756.77 1652 1514.98];
% s1=find(IDXO==1);%聚类号为1的样品在p中的序号
% s11=p(s1,:)
s4=find(IDXO==4);%聚类号为4的样品在p中的序号
s44=p(s4,:);%全部为4类的样品矩阵
CO(4,:)=[sum(s44(:,1))/59,sum(s44(:,2))/59,sum(s44(:,3))/59];%第4类的中心
JO=0;
j1=0; j2=0; j3=0; j4=0;
for i=1:num
if IDXO(i)==4
j4=j4+sqrt((p(i,1)-CO(1,1))^2+(p(i,2)-CO(1,2))^2+(p(i,3)-CO(1,3))^2);
end
end
Three, running results
Complete code or write on behalf of adding QQ1575304183