You mean
objective = @(x) 1.002 - ((1 - exp(-0.00003 * x)) ./ (0.00003 * (x + 1.5) + 0.00063 * (1 - exp(-0.00003 * x))));
a = 24;
b = 720;
%[a,b]=[24:720];
options = optimoptions(@ga, 'PopulationSize', 50, 'MaxGenerations', 100, 'Display', 'iter');
%options = optimoptions('ga', 'PopulationSize', 50, 'MaxGenerations', 100, 'Display', 'iter');
[x_opt, fval] = ga(objective, 1, [], [], [], [], a, b, [], options)
Single objective optimization:
1 Variables
Options:
CreationFcn: @gacreationuniform
CrossoverFcn: @crossoverscattered
SelectionFcn: @selectionstochunif
MutationFcn: @mutationadaptfeasible
Best Mean Stall
Generation Func-count f(x) f(x) Generations
1 100 0.01208 0.01488 0
2 147 0.01208 0.01327 0
3 194 0.01208 0.0131 0
4 241 0.01208 0.01302 0
5 288 0.01208 0.0126 1
6 335 0.01208 0.0121 0
7 382 0.01207 0.01208 0
8 429 0.01207 0.01208 1
9 476 0.01207 0.01208 2
10 523 0.01207 0.01208 0
11 570 0.01207 0.01207 0
12 617 0.01207 0.01207 0
13 664 0.01207 0.01207 1
14 711 0.01207 0.01207 0
15 758 0.01207 0.01207 1
16 805 0.01207 0.01207 0
17 852 0.01207 0.01207 0
18 899 0.01206 0.01207 0
19 946 0.01206 0.01207 1
20 993 0.01206 0.01207 2
21 1040 0.01206 0.01207 0
22 1087 0.01206 0.01207 0
23 1134 0.01206 0.01207 0
24 1181 0.01206 0.01206 0
25 1228 0.01206 0.01206 0
26 1275 0.01206 0.01206 0
27 1322 0.01206 0.01206 1
28 1369 0.01206 0.01206 0
29 1416 0.01206 0.01206 1
30 1463 0.01206 0.01206 2
Best Mean Stall
Generation Func-count f(x) f(x) Generations
31 1510 0.01206 0.01206 0
32 1557 0.01206 0.01206 0
33 1604 0.01206 0.01206 0
34 1651 0.01206 0.01206 1
35 1698 0.01206 0.01206 0
36 1745 0.01205 0.01206 0
37 1792 0.01205 0.01206 0
38 1839 0.01205 0.01206 0
39 1886 0.01205 0.01205 1
40 1933 0.01205 0.01205 0
41 1980 0.01205 0.01205 0
42 2027 0.01205 0.01205 0
43 2074 0.01205 0.01205 0
44 2121 0.01205 0.01205 0
45 2168 0.01205 0.01205 1
46 2215 0.01205 0.01205 0
47 2262 0.01205 0.01205 0
48 2309 0.01205 0.01205 0
49 2356 0.01205 0.01205 0
50 2403 0.01205 0.01205 0
51 2450 0.01205 0.01205 0
ga stopped because the average change in the fitness value is less than options.FunctionTolerance.
x_opt = 322.6320
fval = 0.0120
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