28954437142

Committed 08 Jul 2026 03:24PM UTC coverage: 87.552% (+0.3%) from 87.28%

Build # 28954437142

Build Type

Pull #23

github

Committed by

web-flow

Commit Message

Merge 15e9c59e8 into acbef679d

Pull Request Pull Request #23: feat: add MO-SEGO acquisition strategy

Coverage Stats

665 of 773 new or added lines in 17 files covered. (86.03%)

3 existing lines in 1 file now uncovered.

4403 of 5029 relevant lines covered (87.55%)

1.75 hits per line

Source File
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98.61

/src/smt_optim/benchmarks/multiobj/constrained.py

import numpy as np

from smt_optim.benchmarks.base import BenchmarkProblem


class BNH(BenchmarkProblem):
    def __init__(self):
        super().__init__()

        self.name: str = "BNH"

        self.num_dim: int = 2
        self.num_obj: int = 2
        self.num_cstr: int = 2
        self.num_fidelity = 1

        self.tags = [
            "multi-obj",
        ]

        self.bounds = np.array(
            [
                [0, 5],
                [0, 3],
            ]
        )

        # original bounds
        # bounds = np.array([[-15, 30]] * 2)

        self.objective = [
            self.f1,
            self.f2,
        ]

        self.constraints = [
            self.g1,
            self.g2,
        ]

    def f1(self, x):
        return 4 * x[0] ** 2 + 4 * x[1] ** 2

    def f2(self, x):
        return (x[0] - 5) ** 2 + (x[1] - 5) ** 2

    def g1(self, x):
        return (x[0] - 5) ** 2 + x[1] ** 2 - 25

    def g2(self, x):
        return -((x[0] - 8) ** 2) - (x[1] + 3) ** 2 + 7.7


class TNK(BenchmarkProblem):
    def __init__(self):
        super().__init__()

        self.name: str = "TNK"

        self.num_dim: int = 2
        self.num_obj: int = 2
        self.num_cstr: int = 2
        self.num_fidelity = 1

        self.tags = [
            "multi-obj",
        ]

        self.bounds = np.array(
            [
                [0, np.pi],
                [0, np.pi],
            ]
        )

        self.objective = [
            self.f1,
            self.f2,
        ]

        self.constraints = [
            self.g1,
            self.g2,
        ]

    def f1(self, x):
        return x[0]

    def f2(self, x):
        return x[1]

    def g1(self, x):
        return -(x[0] ** 2) - x[1] ** 2 + 1 + 0.1 * np.cos(16 * np.arctan(x[0] / x[1]))

    def g2(self, x):
        return (x[0] - 0.5) ** 2 + (x[1] - 0.5) ** 2 - 0.5


class OSY(BenchmarkProblem):
    def __init__(self):
        super().__init__()

        self.name: str = "OSY"

        self.num_dim: int = 6
        self.num_obj: int = 2
        self.num_cstr: int = 6
        self.num_fidelity = 1

        self.tags = [
            "multi-obj",
        ]

        self.bounds = np.array(
            [
                [0.0, 10.0],
                [0.0, 10.0],
                [0.0, 5.0],
                [0.0, 6.0],
                [0.0, 5.0],
                [0.0, 10.0],
            ]
        )

        self.objective = [
            self.f1,
            self.f2,
        ]

        self.constraints = [
            self.g1,
            self.g2,
            self.g3,
            self.g4,
            self.g5,
            self.g6,
        ]

    def f1(self, x):
        return -(
            25 * (x[0] - 2) ** 2
            + (x[1] - 2) ** 2
            + (x[2] - 1) ** 2
            + (x[3] - 4) ** 2
            + (x[4] - 1) ** 2
        )

    def f2(self, x):
        return np.sum(x**2)

    def g1(self, x):
        return -x[0] - x[1] + 2

    def g2(self, x):
        return -6 + x[0] + x[1]

    def g3(self, x):
        return -2 + x[1] - x[0]

    def g4(self, x):
        return -2 + x[0] - 3 * x[1]

    def g5(self, x):
        return -4 + (x[2] - 3) ** 2 + x[3]

    def g6(self, x):
        return -((x[4] - 3) ** 2) - x[5] + 4


if __name__ == "__main__":
    pass

1	import numpy as np	2✔
2
3	from smt_optim.benchmarks.base import BenchmarkProblem	2✔
4
5
6	class BNH(BenchmarkProblem):	2✔
7	def __init__(self):	2✔
8	super().__init__()	2✔
9
10	self.name: str = "BNH"	2✔
11
12	self.num_dim: int = 2	2✔
13	self.num_obj: int = 2	2✔
14	self.num_cstr: int = 2	2✔
15	self.num_fidelity = 1	2✔
16
17	self.tags = [	2✔
18	"multi-obj",
19	]
20
21	self.bounds = np.array(	2✔
22	[
23	[0, 5],
24	[0, 3],
25	]
26	)
27
28	# original bounds
29	# bounds = np.array([[-15, 30]] * 2)
30
31	self.objective = [	2✔
32	self.f1,
33	self.f2,
34	]
35
36	self.constraints = [	2✔
37	self.g1,
38	self.g2,
39	]
40
41	def f1(self, x):	2✔
42	return 4 * x[0] ** 2 + 4 * x[1] ** 2	2✔
43
44	def f2(self, x):	2✔
45	return (x[0] - 5) 2 + (x[1] - 5) 2	2✔
46
47	def g1(self, x):	2✔
48	return (x[0] - 5) 2 + x[1] 2 - 25	2✔
49
50	def g2(self, x):	2✔
51	return -((x[0] - 8) 2) - (x[1] + 3) 2 + 7.7	2✔
52
53
54	class TNK(BenchmarkProblem):	2✔
55	def __init__(self):	2✔
56	super().__init__()	2✔
57
58	self.name: str = "TNK"	2✔
59
60	self.num_dim: int = 2	2✔
61	self.num_obj: int = 2	2✔
62	self.num_cstr: int = 2	2✔
63	self.num_fidelity = 1	2✔
64
65	self.tags = [	2✔
66	"multi-obj",
67	]
68
69	self.bounds = np.array(	2✔
70	[
71	[0, np.pi],
72	[0, np.pi],
73	]
74	)
75
76	self.objective = [	2✔
77	self.f1,
78	self.f2,
79	]
80
81	self.constraints = [	2✔
82	self.g1,
83	self.g2,
84	]
85
86	def f1(self, x):	2✔
87	return x[0]	2✔
88
89	def f2(self, x):	2✔
90	return x[1]	2✔
91
92	def g1(self, x):	2✔
93	return -(x[0] 2) - x[1] 2 + 1 + 0.1 * np.cos(16 * np.arctan(x[0] / x[1]))	2✔
94
95	def g2(self, x):	2✔
96	return (x[0] - 0.5) 2 + (x[1] - 0.5) 2 - 0.5	2✔
97
98
99	class OSY(BenchmarkProblem):	2✔
100	def __init__(self):	2✔
101	super().__init__()	2✔
102
103	self.name: str = "OSY"	2✔
104
105	self.num_dim: int = 6	2✔
106	self.num_obj: int = 2	2✔
107	self.num_cstr: int = 6	2✔
108	self.num_fidelity = 1	2✔
109
110	self.tags = [	2✔
111	"multi-obj",
112	]
113
114	self.bounds = np.array(	2✔
115	[
116	[0.0, 10.0],
117	[0.0, 10.0],
118	[0.0, 5.0],
119	[0.0, 6.0],
120	[0.0, 5.0],
121	[0.0, 10.0],
122	]
123	)
124
125	self.objective = [	2✔
126	self.f1,
127	self.f2,
128	]
129
130	self.constraints = [	2✔
131	self.g1,
132	self.g2,
133	self.g3,
134	self.g4,
135	self.g5,
136	self.g6,
137	]
138
139	def f1(self, x):	2✔
140	return -(	2✔
141	25 * (x[0] - 2) ** 2
142	+ (x[1] - 2) ** 2
143	+ (x[2] - 1) ** 2
144	+ (x[3] - 4) ** 2
145	+ (x[4] - 1) ** 2
146	)
147
148	def f2(self, x):	2✔
149	return np.sum(x**2)	2✔
150
151	def g1(self, x):	2✔
152	return -x[0] - x[1] + 2	2✔
153
154	def g2(self, x):	2✔
155	return -6 + x[0] + x[1]	2✔
156
157	def g3(self, x):	2✔
158	return -2 + x[1] - x[0]	2✔
159
160	def g4(self, x):	2✔
161	return -2 + x[0] - 3 * x[1]	2✔
162
163	def g5(self, x):	2✔
164	return -4 + (x[2] - 3) ** 2 + x[3]	2✔
165
166	def g6(self, x):	2✔
167	return -((x[4] - 3) ** 2) - x[5] + 4	2✔
168
169
170	if __name__ == "__main__":	2✔
NEW 171	pass	×

SMTorg / smt-optim / 28954437142

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