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Application of Recursive Perturbation Approach for
Multimodal Optimization (RePAMO) for classical
optimization problems
Presented byPritam Bhadra
Pranamesh Chakraborty
Indian Institute of Technology, Kanpur
11 May 2013
Formulation of RePAMO
Multi-start algorithm dealing with variable population
A selected classical optimization method (in this case
Nelder Mead's Simplex Search Method) is recursively applied
to find all optima of a function.
The idea of climbing the hills and sliding down to the
nearby hills is applied.
Three basic operators:
1. Direction Set Generation and Perturbation
2. Optimization
3. Comparison
Results
Constrained Himmelblau function2 2 2 2
2 2
( , ) ( 11) ( 7)
subjected to
x 25
f x y x y x y
y
-5-4
-3-2
-10
12
34
5
-5-4
-3-2
-10
12
34
5
0
100
200
300
400
500
600
700
800
900
Figure 1: 3d plot of Himmelblau function
Constrained Himmelblau function
Figure 2: Connectivity of optima of Constrained Himmelblau finction
Constrained Himmelblau function
Function# Of
Minima
# Of
Maxima
# Of Function
Evaluation
# Of
Generation
Contrained
Himmelblau4 4 113250 6
The global minima is at (-3.775, -3.278) with f=0 and global
maxima at (0.26,-5).
Greiwank function
22
1 1
1( ) cos 1
4000
X={x 600 600 (i=1,2)}
ni
i
i i
i
xf X x
i
x
-50
-40
-30
-20
-10
0
10
20
30
40
50
-50-40-30-20-1001020304050
-1
0
1
2
3
Figure 3: 3d plot of Griewank function
Greiwank function
0.2
11
44
0.21144
0.21144
0.21144
0.2
1144
0.2
11
44
0.2
11
44
0.21144
0.21144
0.2
11
44
0.21144
0.21144
0.8
17
17
0.8
17
17
0.8
17
17
0.817170.8
17
17
0.81717 0.81
717
0.81717
0.8
17
17
0.81717
0.8
17
17
0.81717
0.81717
0.81717
0.81717
0.81717
0.81717
0.81717
0.81717
0.81717
1.4
229
1.4
229
1.4
229
1.4
229
-60 -40 -20 0 20 40 60-60
-40
-20
0
20
40
60
Figure 4: Contour plot of Greiwank function
Greiwank function
Function# Of
Minima
# Of
Maxima
# Of Function
Evaluation
# Of
Generation
Greiwank
function1780 480 129,970 7
The global minima is at (0,0) with f=0 and global maxima at (600,600)
Schwefel function
2
1
( ) sin
X={x 500 500 (i=1,2)}
i i
i
i
f X x x
x
-50-40
-30-20
-100
1020
3040
50
-50-40
-30-20
-100
1020
3040
50
-80
-60
-40
-20
0
20
40
60
80
Figure 5: 3d plot of Schwefel function
Schwefel function
-1.4888-1.424-1.3593
-1.2946-1.2298
-1.2298
-1.1651
-1.1651
-1.1004
-1.1004
-1.0357
-1.0357
-1.03
57
-0.97093
-0.97093
-0.97
093
-0.9062
-0.9062
-0.9062
-0.84147
-0.84147
-0.84147
-0.8
4147
-0.7
7674
-0.77674
-0.77674
-0.77674
-0.7
1201
-0.71201
-0.71201
-0.71201
-0.64
729
-0.64729
-0.64729
-0.58
256
-0.58256
-0.58256
-0.5
1783
-0.51783
-0.51783
-0.4
531
-0.4531
-0.4531
-0.38837
-0.38837
-0.32364
-0.32364
-0.25
891
-0.25891
-0.19419
-0.12946
-0.064729
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Figure 6: Contour plot of Schwefel function
Schwefel function
Function# Of
Minima
# Of
Maxima
# Of Function
Evaluation
# Of
Generation
Schwefel
function19 14 198,978 8
The global minima is at (421,421) and global maxima at
(302.565,302.565)
Guilin Hills function
2
1
9( ) 3 sin
1101
2
X={x 0 1 (i=1,2)}
ii
i ii
i
i
xf X c
xx
k
x
00.1
0.20.3
0.40.5
0.60.7
0.80.9
1
00.1
0.20.3
0.40.5
0.60.7
0.80.9
1
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
Figure 7: 3d plot of Guilin Hills function
Guilin Hills function
1.1
41
1
1.1411
1.1411
1.5
20
5
1.5205
1.5205
1.5
20
5
1.5
205
1.5205
1.5205
1.8999
1.8999
1.8
999
1.8999
1.8999
1.8999
1.8
99
9
1.8999
1.8999 1.8
99
91.8999 1.8
99
9
2.2793
2.2793
2.2793
2.2793
2.2793
2.2793
2.27932.2793
2.2
793
2.2793
2.2793
2.2793
2.2
79
3 2.2793
2.65872.6587
2.6587
2.65872.6587
2.6587
2.6587
2.6587
2.6587
2.6587
2.6587
2.6587
2.6
587
2.6
58
7
2.6587
3.0381
3.0
381
3.0
38
1
3.0381
3.0381
3.03813.0381
3.0381
3.0381
3.0381
3.0381
3.0381
3.4176 3.4
17
6 3.4
17
6 3.4176
3.4176
3.41763.4176
3.4176
3.4176
3.7
97
3.797 3.797
3.7
97
3.797
4.1
764
4.1
76
4
4.5
55
8
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Figure 8: Contour plot of Guilin Hills function
Guilin Hills function
Function# Of
Minima
# Of
Maxima
# Of Function
Evaluation
# Of
Generation
Guilin Hills
function15 25 278,978 6
The global minima is at (420.969,420.969)
Six Hump Camel function
62 4 2 411 1 1 2 2 2
1
2
( ) 4 2.1 4 43
: 2 2
1 1
xf X x x x x x x
for x
x
-2-1.5
-1-0.5
00.5
11.5
2
-1-0.8
-0.6-0.4
-0.20
0.20.4
0.60.8
1
-2
-1
0
1
2
3
4
5
6
Figure 10: 3d plot of Six Hump Camel function
Six Hump Camel function
-0.8367
9
-0.67399
-0.67399
-0.5
1118
-0.51118
-0.34838
-0.3
4838
-0.34838
-0.18557
-0.1
8557
-0.18557
-0.022769
-0.0
22769
-0.0227
69
-0.022769
0.14004
0.1
40
04
0.14
004
0.30284
0.3
0284
0.3
02
84
0.46565
0.4
65
65
0.4
65
65
0.62845
0.6
28
45
0.6
28
45
0.79126
0.7
91
26
0.7
91
26
0.95406
0.9
54
06
0.9
54
06
1.1169
1.1
16
9
1.1
16
9
1.2797
1.2
79
7
1.2
79
7
1.4425
1.4
42
5
1.4
42
5
1.6053
1.6
05
3
1.6
05
3
1.7681
1.7
68
1
1.7
68
1
1.9309
1.9
30
9
1.9
30
9
2.0937
2.0
937
2.2565
2.2
56
5
2.4193
2.5821
2.7449
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Figure 11: Contour plot of Six Hump Camel function
Six Hump Camel function
Function# Of
Minima
# Of
Maxima
# Of Function
Evaluation
# Of
Generation
Six Hump
Camel
function
6 12 72,563 5
Higher dimensional problems
Rastrigin 3d function
2
1
( ) (10 10cos(2 )
to
0.5 1.5
n
i i
i
i
f x x x
subjected
x
Function# Of
Minima
# Of
Maxima
# Of Function
Evaluation
# Of
Generation
Rastrigin 3d
function8 15 18,670 5
The global minima is at (0,0,0) with f=0 and global maxima at
(1.5,1.5,1.5)
Higher dimensional problems
Ackley 4d function
Function# Of
Minima
# Of
Maxima
# Of Function
Evaluation
# Of
Generation
Ackley 4d
function333 378 12,624,183 9
The global minima is at (0,0,0,0) with f=0 and global maxima at
(2,1.6,1.6)
2
1 1
1 10.2 cos(2 )
( ) 20 20 (i=1,2,....n)
X={x 2 2 (i=1,2,....n)}
n n
i i
i i
x xn n
i
f X e e e
x
Higher dimensional problems
Michalewicz 5d function
Function# Of
Minima
# Of
Maxima
# Of Function
Evaluation
# Of
Generation
Michalewicz
5d function23,962 3,240 30,102,813 7
The global minima is at (2.203,1.571,1.285,1.114,1.72)
225
1
( ) sin sin
X={x 0 1 (i=1,5)}, m=10
m
ii
i
i
ixf X x
x
Function# of
Minima
# of
Maxima
# of Function
Evaluation# of Generation
Contrained
Himmelblau4 4 113250 6
Griewank 1780 480 1,29,970 7
Schwefel 14 19 1,98,978 8
Guilinhills 15 25 2,78,978 6
SixHumpCamel 6 12 72,563 5
Rastrigin 3-D 8 15 18,670 5
Ackley 4-D 333 378 1,26,24,183 9
Michalewicz 5-D 23962 3240 3,01,02,813 7
The algorithm worked successfully for all functions considered in this case.
Conclusions
Summary of results obtained
References
1. Bhaskar Dasgupta , Kotha Divya , Vivek Kumar Mehta & Kalyanmoy Deb
(2012):RePAMO: Recursive Perturbation Approach for Multimodal Optimization,
Engineering Optimization, DOI:10.1080/0305215X.2012.725050 aDepartment of
Mechanical engineering, IIT Kapur; bISRO Sattelite Centre, Bangalore.
THANK YOU