-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathSIPO.m
169 lines (141 loc) · 6.08 KB
/
SIPO.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
%% Iteration-baed SIPO algorithm for INS/GPS navigation
% Ali Mohammadi_INS/GNSS
clc ;
clear all ;
close all ;
% format shortg
prompt = {'Please enter the number of run:'} ;
title = 'SIPO Algorithm' ;
dims = [1 45] ;
nline = 1 ;
definput = {'1','a'} ;
answer = inputdlg(prompt,title,dims,definput) ;
Run_Num = answer(1,:) ;
Run_Num = str2num(Run_Num{:}) ;
prompt = {'maxt','npop' ,'F','Beta','c','m_Ratio'} ;
title = 'SIPO parameters' ;
nline = 1 ;
dims = [1 45] ;
definput = {'500','50' '1','1','2','0.1','a'} ;
answer = inputdlg(prompt,title,dims,definput) ;
maxt = answer(1,:); maxt = str2num(maxt{:}) ;
npop = answer(2,:); npop = str2num(npop{:}) ;
F = answer(3,:); F = str2num(F{:}) ;
Beta = answer(4,:); Beta = str2num(Beta{:}) ;
c = answer(5,:); c = str2num(c{:}) ;
m_Ratio = answer(6,:); m_Ratio = str2num(m_Ratio{:}) ;
n = 0 ;
Bests = zeros(1 , Run_Num ) ;
BestsPop = zeros(Run_Num , 18 ) ; %varaible number
BestsCnvg = zeros(Run_Num , 500 ) ; % numofruns
RunTime = zeros(1 , Run_Num ) ;
NoU_index = zeros(Run_Num , 1 ) ;
Stable = 0 ;
NoU = 0 ;
for n = 1:Run_Num
tic
n
nvar = 18 ;
varsize = [1 nvar] ;
varmin = [0 0 0 0 0 0 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10] ; %%%% Define lowband
varmax = [1 1 1 100 100 100 1 1 1 1 1 1 1 1 1 1 1 1 ] ; %%%% Define upperband
% varmin = -1.2 ;
% varmax = +1.2 ;
%% SIPO parameters
% npop = 50;
% maxt = 200;
%%
empty_ball.position = [] ;
empty_ball.cost = [] ;
empty_ball.velocity = [] ;
empty_ball.acceleration = [] ;
ball = repmat(empty_ball,npop,1) ;
globalbest.cost = inf ;
for i = 1:npop
ball(i).position = unifrnd(varmin,varmax) ;
ball(i).velocity = zeros(varsize) ;
ball(i).Acceleration = zeros(varsize) ;
ball(i).sbetter = zeros(varsize) ;
ball(i).mean = zeros(varsize) ;
ball(i).cost = fitness1(ball(i).position) ;
if ball(i).cost < globalbest.cost
globalbest.position = ball(i).position ;
globalbest.cost = ball(i).cost ;
end
end
bests = zeros(maxt,1) ;
T = m_Ratio.*maxt ;
%%
for t = 1:maxt
sumcost = 0 ;
s = 1 ;
for i= 1:npop
ball(i).sbetter = ball(i).position ;
for j= 1:npop
df = ball(j).cost - ball(i).cost ;
if df < 0
ball(i).sbetter = ball(i).sbetter + ball(j).position ;
s = s+1 ;
end
end
ball(i).mean = ((ball(i).sbetter) ./ s) ;
P_MEAN = F.*(maxt./t);
k1 = (1./t)^(Beta) ;
k2 = c ./ (1 + exp( - (t-T)));
ball(i).velocity = globalbest.position-ball(i).position;
ball(i).Acceleration = P_MEAN .* ball(i).mean - ball(i).position;
ball(i).position = ball(i).position + ...
k1 .* (ball(i).Acceleration) .* rand(varsize)+...
k2 .* ball(i).velocity .* rand(varsize);
ball(i).position = min(max(ball(i).position,varmin),varmax);
ball(i).cost = fitness1(ball(i).position);
if ball(i).cost < globalbest.cost
globalbest.position = ball(i).position;
globalbest.cost = ball(i).cost;
end
bests(t) = globalbest.cost;
sumcost = sumcost+ball(i).cost;
end
disp(['Iteration' num2str(t) ':bestcost=' num2str(bests(t))]);
meanfits(t) = sumcost/npop;
t
end
BestsCnvg(n,:) = bests ;
Bests(n) = bests(t-1) ;
BestsPop(n,:) = globalbest.position ;
RunTime(n) = toc ;
end
% disp([' ']);
disp([' ']);
disp([' SIPO ']);
disp(['-----------------------------------------------']);
disp(['Number of run = ' num2str(Run_Num)]);
disp([' ']);
disp([' ']);
disp(['**************** Statistical indexes : Time ****************']);
disp(['------------------------------------------------']);
disp(['Per run = ' num2str(RunTime)]);
disp(['Average = ' num2str(mean(RunTime))]);
disp(['Standard deviation = ' num2str(std(RunTime))]);
disp(['Maximum = ' num2str(max(RunTime))]);
disp(['Minimum = ' num2str(min(RunTime))]);
% disp([' ']);
disp([' ']);
disp(['***************** Statistical indexes : Fitness ****************']);
disp(['-----------------------------------------------']);
disp(['Number of run = ' num2str(Run_Num)]);
disp(['Best cost per run = ' num2str(Bests)]);
disp(['Average = ' num2str(mean(Bests))]);
disp(['Standard deviation = ' num2str(std(Bests))]);
disp(['Maximum = ' num2str(max(Bests))]);
disp(['Minimum = ' num2str(min(Bests))]);
%% SIPO *******************************
[minimum index] = min(Bests);
disp([ ' Best Solution = ' num2str(BestsPop(index,:))])
figure(1);
plot(BestsCnvg(index,:),'.b','LineWidth',1);
legend('Bests_SIPO')
xlabel('Iteration')
ylabel('Fitness')
fitness2(BestsPop(index,:))
legend('Bests_SIPO')