enoppy.paper_based

enoppy.paper_based.ihaoavoa_2022

enoppy.paper_based.ihaoavoa_2022.CBP

alias of enoppy.paper_based.ihaoavoa_2022.CantileverBeamProblem

class enoppy.paper_based.ihaoavoa_2022.CantileverBeamProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [x1, x2, x3, x4, x5]

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Cantilever beam design problem'

enoppy.paper_based.ihaoavoa_2022.REBP

alias of enoppy.paper_based.ihaoavoa_2022.RollingElementBearingProblem

class enoppy.paper_based.ihaoavoa_2022.RollingElementBearingProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [x1, x2, x3, x4, x5, x6, x7, x8, x9, x10] = [Dm, Db, Z, fi, f0, Kdmin, Kdmax, theta, e, C]

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Rolling element bearing design problem'

enoppy.paper_based.ihaoavoa_2022.SRP

alias of enoppy.paper_based.ihaoavoa_2022.SpeedReducerProblem

class enoppy.paper_based.ihaoavoa_2022.SpeedReducerProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [x1, x2, x3, x4, x5, x6, x7]

Ref: https://www.hindawi.com/journals/mpe/2013/419043/

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Speed reducer design problem'

enoppy.paper_based.ihaoavoa_2022.TCSP

alias of enoppy.paper_based.ihaoavoa_2022.TensionCompressionSpringProblem

class enoppy.paper_based.ihaoavoa_2022.TensionCompressionSpringProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [x1, x2, x3] = [d, D, N]

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Tension/compression spring design problem'

enoppy.paper_based.ihaoavoa_2022.WBP

alias of enoppy.paper_based.ihaoavoa_2022.WeldedBeamProblem

class enoppy.paper_based.ihaoavoa_2022.WeldedBeamProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [x1, x2, x3, x4] = [h, l, t, b]

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Welded beam design problem'

enoppy.paper_based.moeosma_2023

enoppy.paper_based.moeosma_2023.BCP

alias of enoppy.paper_based.moeosma_2023.BulkCarriersProblem

class enoppy.paper_based.moeosma_2023.BulkCarriersProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [L, B, D, T, Vk, CB] = [x1, x2, x3, x4, x5, x6]

Original Ref:

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Bulk carriers design problem'

enoppy.paper_based.moeosma_2023.CSP

alias of enoppy.paper_based.moeosma_2023.CarSideImpactProblem

class enoppy.paper_based.moeosma_2023.CarSideImpactProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [x1, x2, x3, x4, x5, x6, x7]

Original Ref:

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Car side impact design problem'

enoppy.paper_based.moeosma_2023.HTBP

alias of enoppy.paper_based.moeosma_2023.HydrostaticThrustBearingProblem

class enoppy.paper_based.moeosma_2023.HydrostaticThrustBearingProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [x1, x2, x3, x4] = [R, R0, mu, Q]

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Hydrostatic thrust bearing design problem'

enoppy.paper_based.moeosma_2023.MPBPP

alias of enoppy.paper_based.moeosma_2023.MultiProductBatchPlantProblem

class enoppy.paper_based.moeosma_2023.MultiProductBatchPlantProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [N1, N2, N3, V1, V2, V3, TL1, TL2, B1, B2] = [x1, x2, x3, x4, x5, x6, x7, x8, x9, x10]

Original Ref:

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Multi-product batch plant problem'

enoppy.paper_based.moeosma_2023.SP

alias of enoppy.paper_based.moeosma_2023.SpringProblem

enoppy.paper_based.moeosma_2023.SRP

alias of enoppy.paper_based.moeosma_2023.SpeedReducerProblem

class enoppy.paper_based.moeosma_2023.SpeedReducerProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [x1, x2, x3, x4, x5, x6, x7] = [b, m, z, l1, l2, d1, d2]

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Speed Reducer Design Problem'

class enoppy.paper_based.moeosma_2023.SpringProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [x1, x2, x3] = [d, D, N]

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Spring Design Problem'

enoppy.paper_based.moeosma_2023.VPP

alias of enoppy.paper_based.moeosma_2023.VibratingPlatformProblem

class enoppy.paper_based.moeosma_2023.VibratingPlatformProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [d1, d2, d3, b, L] = [x0, x1, x2, x3, x4]

Original Ref: On improving multiobjective genetic algorithms for design optimization

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Vibrating platform design problem'

enoppy.paper_based.moeosma_2023.WRMP

alias of enoppy.paper_based.moeosma_2023.WaterResourceManagementProblem

class enoppy.paper_based.moeosma_2023.WaterResourceManagementProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [x1, x2, x3]

Original Ref:

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Water resource management problem'

enoppy.paper_based.pdo_2022

enoppy.paper_based.pdo_2022.CBD

alias of enoppy.paper_based.pdo_2022.CantileverBeamProblem

enoppy.paper_based.pdo_2022.CBHD

alias of enoppy.paper_based.pdo_2022.CorrugatedBulkheadProblem

enoppy.paper_based.pdo_2022.CSP

alias of enoppy.paper_based.pdo_2022.CompressionSpringProblem

class enoppy.paper_based.pdo_2022.CantileverBeamProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Minimize a cantilever beam’s weight.

[x1, x2, x3, x4, x5]

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Cantilever Beam Design Problem'

class enoppy.paper_based.pdo_2022.CompressionSpringProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [x1, x2, x3, x4]

CSD aims to minimize the weight of a tension/compression spring given the values of 3 parameters:

the wire diameter (d=x1), number of active coils (P=x3), and mean coil diameter (D=x2).

https://sci-hub.se/10.1016/s0166-3615(99)00046-9

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Compression Spring Design Problem'

class enoppy.paper_based.pdo_2022.CorrugatedBulkheadProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

[x1, x2, x3, x4] = [width, depth, length, thickness]

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Corrugated Bulkhead Design Problem'

enoppy.paper_based.pdo_2022.GTD

alias of enoppy.paper_based.pdo_2022.GearTrainProblem

class enoppy.paper_based.pdo_2022.GearTrainProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Unconstrained discrete design optimization problem

[x1, x2, x3, x4] = [n_A, n_B, n_C, n_D]

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Gear Train Design Problem'

enoppy.paper_based.pdo_2022.IBD

alias of enoppy.paper_based.pdo_2022.IBeamProblem

class enoppy.paper_based.pdo_2022.IBeamProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Minimizes the vertical deflection of a beam

[x1, x2, x3, x4] = [b, h, t_w, t_f]

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'I Beam Design Problem'

enoppy.paper_based.pdo_2022.PLD

alias of enoppy.paper_based.pdo_2022.PistonLeverProblem

enoppy.paper_based.pdo_2022.PVP

alias of enoppy.paper_based.pdo_2022.PressureVesselProblem

class enoppy.paper_based.pdo_2022.PistonLeverProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

[x1, x2, x3, x4] = [H, B, D, X]

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Piston Lever Design Problem'

class enoppy.paper_based.pdo_2022.PressureVesselProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [x1, x2, x3, x4]

Variables: the inner radius (R=x3), the thickness of the head (Th=x2),

the length of the cylindrical section of the vessel (L=x4), and the thickness of the shell (Ts=x1)

https://sci-hub.se/10.1115/1.2912596

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Pressure Vessel Design Problem'

enoppy.paper_based.pdo_2022.RCB

alias of enoppy.paper_based.pdo_2022.ReinforcedConcreateBeamProblem

class enoppy.paper_based.pdo_2022.ReinforcedConcreateBeamProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

[x1, x2, x3]

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Reinforced Concreate Beam Design Problem'

enoppy.paper_based.pdo_2022.SRD

alias of enoppy.paper_based.pdo_2022.SpeedReducerProblem

class enoppy.paper_based.pdo_2022.SpeedReducerProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Depicts a gearbox that sits between the propeller and engine of an aeroplane [x1, x2, x3, x4, x5, x6, x7] = [b, m, z, l1, l2, d1, d2]

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Speed Reducer Design Problem'

enoppy.paper_based.pdo_2022.TBTD

alias of enoppy.paper_based.pdo_2022.ThreeBarTrussProblem

enoppy.paper_based.pdo_2022.TCD

alias of enoppy.paper_based.pdo_2022.TubularColumnProblem

class enoppy.paper_based.pdo_2022.ThreeBarTrussProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Minimize three-bar structure weight subject to supporting a total load P acting vertically downwards

[x1, x2]

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Three Bar Truss Design Problem'

class enoppy.paper_based.pdo_2022.TubularColumnProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

[x1, x2] = [d, t]

https://apmonitor.com/me575/index.php/Main/TubularColumn

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Tubular Column Design Problem'

enoppy.paper_based.pdo_2022.WBP

alias of enoppy.paper_based.pdo_2022.WeldedBeamProblem

class enoppy.paper_based.pdo_2022.WeldedBeamProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

x = [x1, x2, x3, x4]

WBD is subjected to 4 design constraints: shear, beam blending stress, bar buckling load beam, and deflection variables: h=x1, l=x2, t=x3, b=x4 l: length, h: height, t: thickness, b: weld thickness of the bar

https://sci-hub.se/10.1016/s0166-3615(99)00046-9

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Welded Beam Design Problem'

enoppy.paper_based.rwco_2020

enoppy.paper_based.rwco_2020.BPSP

alias of enoppy.paper_based.rwco_2020.BlendingPoolingSeparationProblem

class enoppy.paper_based.rwco_2020.BlendingPoolingSeparationProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Industrial Chemical Processes [x1, x2, x3, x4,…, x37, x38] Blending-Pooling-Separation problem

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_eq_cons(x)

Compute the values of the equality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Blending-Pooling-Separation problem (Industrial Chemical Processes)'

enoppy.paper_based.rwco_2020.CCSDP

alias of enoppy.paper_based.rwco_2020.TensionCompressionSpringDesignProblem

enoppy.paper_based.rwco_2020.HENDC1P

alias of enoppy.paper_based.rwco_2020.HeatExchangerNetworkDesignCase1Problem

enoppy.paper_based.rwco_2020.HENDC2P

alias of enoppy.paper_based.rwco_2020.HeatExchangerNetworkDesignCase2Problem

enoppy.paper_based.rwco_2020.HPP

alias of enoppy.paper_based.rwco_2020.HaverlyPoolingProblem

class enoppy.paper_based.rwco_2020.HaverlyPoolingProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Industrial Chemical Processes [x1, x2, x3, x4,…, x9] Haverly’s Pooling Problem

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_eq_cons(x)

Compute the values of the equality constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Maximum to minimum by using negative sign

name = "Haverly's Pooling Problem (Industrial Chemical Processes)"

class enoppy.paper_based.rwco_2020.HeatExchangerNetworkDesignCase1Problem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Industrial Chemical Processes [x1, x2, x3, x4,…, x9] Heat Exchanger Network Design (case 1)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_eq_cons(x)

Compute the values of the equality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Heat Exchanger Network Design Case 1 (Industrial Chemical Processes)'

class enoppy.paper_based.rwco_2020.HeatExchangerNetworkDesignCase2Problem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Industrial Chemical Processes [x1, x2, x3, x4,…, x10, x11] Heat Exchanger Network Design (case 2)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_eq_cons(x)

Compute the values of the equality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Heat Exchanger Network Design Case 2 (Industrial Chemical Processes)'

enoppy.paper_based.rwco_2020.MDCBDP

alias of enoppy.paper_based.rwco_2020.MultipleDiskClutchBrakeDesignProblem

enoppy.paper_based.rwco_2020.MPBP

alias of enoppy.paper_based.rwco_2020.MultiProductBatchPlantProblem

class enoppy.paper_based.rwco_2020.MultiProductBatchPlantProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Process design and synthesis problems [x1, x2,…, x10] Multi-product batch plant

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Multi-product batch plant (Process design and synthesis problems)'

class enoppy.paper_based.rwco_2020.MultipleDiskClutchBrakeDesignProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Mechanical design problems [x1, x2, x3, x4, x5] Multiple disk clutch brake design problem

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Multiple disk clutch brake design problem (Mechanical design problems)'

enoppy.paper_based.rwco_2020.OBJ11(x, n)

enoppy.paper_based.rwco_2020.ODIRSP

alias of enoppy.paper_based.rwco_2020.OptimalDesignIndustrialRefrigerationSystemProblem

enoppy.paper_based.rwco_2020.OOAUP

alias of enoppy.paper_based.rwco_2020.OptimalOperationAlkylationUnitProblem

class enoppy.paper_based.rwco_2020.OptimalDesignIndustrialRefrigerationSystemProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Mechanical design problems [x1, x2,…, x14] Optimal design of industrial refrigeration system

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Optimal design of industrial refrigeration system (Mechanical design problems)'

class enoppy.paper_based.rwco_2020.OptimalOperationAlkylationUnitProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Industrial Chemical Processes [x1, x2, x3, x4,…, x7] Optimal Operation of Alkylation Unit

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Optimal Operation of Alkylation Unit (Industrial Chemical Processes)'

enoppy.paper_based.rwco_2020.PDP

alias of enoppy.paper_based.rwco_2020.ProcessDesignProblem

enoppy.paper_based.rwco_2020.PFSP

alias of enoppy.paper_based.rwco_2020.ProcessFlowSheetingProblem

enoppy.paper_based.rwco_2020.PGTDOP

alias of enoppy.paper_based.rwco_2020.PlanetaryGearTrainDesignOptimizationProblem

enoppy.paper_based.rwco_2020.PINBNSP

alias of enoppy.paper_based.rwco_2020.PropaneIsobutaneNButaneNonsharpSeparationProblem

enoppy.paper_based.rwco_2020.PS01P

alias of enoppy.paper_based.rwco_2020.ProcessSynthesis01Problem

enoppy.paper_based.rwco_2020.PS02P

alias of enoppy.paper_based.rwco_2020.ProcessSynthesis02Problem

enoppy.paper_based.rwco_2020.PSADP

alias of enoppy.paper_based.rwco_2020.ProcessSynthesisAndDesignProblem

enoppy.paper_based.rwco_2020.PVDP

alias of enoppy.paper_based.rwco_2020.PressureVesselDesignProblem

class enoppy.paper_based.rwco_2020.PlanetaryGearTrainDesignOptimizationProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Mechanical design problems [x1, x2,…,x9] Planetary gear train design optimization problem

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_eq_cons(x)

Compute the values of the equality constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Planetary gear train design optimization problem (Mechanical design problems)'

class enoppy.paper_based.rwco_2020.PressureVesselDesignProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Mechanical design problems [x1, x2, x3, x4] Pressure vessel design

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Pressure vessel design (Mechanical design problems)'

class enoppy.paper_based.rwco_2020.ProcessDesignProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Process design and synthesis problems [x1, x2,…, x5] Process design Problem

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Process design Problem (Process design and synthesis problems)'

class enoppy.paper_based.rwco_2020.ProcessFlowSheetingProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Process design and synthesis problems [x1, x2, x3] Process flow sheeting problem

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Process flow sheeting problem (Process design and synthesis problems)'

class enoppy.paper_based.rwco_2020.ProcessSynthesis01Problem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Process design and synthesis problems [x1, x2] Process synthesis problem 01

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Process synthesis 01 problem (Process design and synthesis problems)'

class enoppy.paper_based.rwco_2020.ProcessSynthesis02Problem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Process design and synthesis problems [x1, x2,…, x9] Process synthesis problem 02

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Process synthesis 02 problem (Process design and synthesis problems)'

class enoppy.paper_based.rwco_2020.ProcessSynthesisAndDesignProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Process design and synthesis problems [x1, x2, x3] Process synthesis and design problem

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_eq_cons(x)

Compute the values of the equality constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Process synthesis and design problem (Process design and synthesis problems)'

class enoppy.paper_based.rwco_2020.PropaneIsobutaneNButaneNonsharpSeparationProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Industrial Chemical Processes [x1, x2, x3, x4,…, x47, x48] Propane, Isobutane, n-Butane Nonsharp Separation

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_eq_cons(x)

Compute the values of the equality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Propane, Isobutane, n-Butane Nonsharp Separation (Industrial Chemical Processes)'

enoppy.paper_based.rwco_2020.RNDP

alias of enoppy.paper_based.rwco_2020.ReactorNetworkDesignProblem

class enoppy.paper_based.rwco_2020.ReactorNetworkDesignProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Industrial Chemical Processes [x1, x2, x3, x4,…, x6] Reactor Network Design Problem

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_eq_cons(x)

Compute the values of the equality constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Reactor Network Design (Industrial Chemical Processes)'

class enoppy.paper_based.rwco_2020.RobotGripperProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Mechanical design problems [x1, x2, x3, x4, x5] Robot gripper problem

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_eq_cons(x)

Compute the values of the equality constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Robot gripper problem (Mechanical design problems)'

enoppy.paper_based.rwco_2020.SCPP

alias of enoppy.paper_based.rwco_2020.StepConePulleyProblem

class enoppy.paper_based.rwco_2020.StepConePulleyProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Mechanical design problems [x1, x2, x3, x4, x5] Step-cone pulley problem

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_eq_cons(x)

Compute the values of the equality constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Step-cone pulley problem (Mechanical design problems)'

enoppy.paper_based.rwco_2020.TBTDP

alias of enoppy.paper_based.rwco_2020.ThreeBarTrussDesignProblem

enoppy.paper_based.rwco_2020.TRP

alias of enoppy.paper_based.rwco_2020.TwoReactorProblem

class enoppy.paper_based.rwco_2020.TensionCompressionSpringDesignProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Mechanical design problems [x1, x2, x3] Tension/compression spring design

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Tension/compression spring design (Mechanical design problems)'

class enoppy.paper_based.rwco_2020.ThreeBarTrussDesignProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Mechanical design problems [x1, x2] Three-bar truss design problem

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Three-bar truss design problem (Mechanical design problems)'

class enoppy.paper_based.rwco_2020.TwoReactorProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Process design and synthesis problems [x1, x2, …, x8] Two-reactor problem

amend_position(x, lb=None, ub=None)

Amend position to fit the format of the problem

Parameters

x (np.ndarray) – The current position (solution)

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_eq_cons(x)

Compute the values of the equality constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Two-reactor problem (Process design and synthesis problems)'

enoppy.paper_based.rwco_2020.WBDP

alias of enoppy.paper_based.rwco_2020.WeldedBeamDesignProblem

enoppy.paper_based.rwco_2020.WMSRP

alias of enoppy.paper_based.rwco_2020.WeightMinimizationSpeedReducerProblem

class enoppy.paper_based.rwco_2020.WeightMinimizationSpeedReducerProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Mechanical design problems [x1, x2,…, x7] Weight minimization of a speed reducer

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Weight minimization of a speed reducer (Mechanical design problems)'

class enoppy.paper_based.rwco_2020.WeldedBeamDesignProblem(f_penalty=None)

Bases: enoppy.engineer.Engineer

Mechanical design problems [x1, x2, x3, x4] Welded beam design

evaluate(x)

Evaluation of the benchmark function.

Parameters

x (np.ndarray, list, tuple) – The candidate vector for evaluating the benchmark problem. Must have len(x) == self.n_dims.

Returns

val – the evaluated benchmark function

Return type

float

get_cons(x)

Compute the values of the constraint functions for a given set of input values.

get_ineq_cons(x)

Compute the values of the inequality constraint functions for a given set of input values.

get_objs(x)

Compute the values of the objective functions for a given set of input values.

name = 'Welded beam design (Mechanical design problems)'

enoppy.paper_based.rwco_2020.p1

alias of enoppy.paper_based.rwco_2020.HeatExchangerNetworkDesignCase1Problem

enoppy.paper_based.rwco_2020.p10

alias of enoppy.paper_based.rwco_2020.ProcessFlowSheetingProblem

enoppy.paper_based.rwco_2020.p11

alias of enoppy.paper_based.rwco_2020.TwoReactorProblem

enoppy.paper_based.rwco_2020.p12

alias of enoppy.paper_based.rwco_2020.ProcessSynthesis02Problem

enoppy.paper_based.rwco_2020.p13

alias of enoppy.paper_based.rwco_2020.ProcessDesignProblem

enoppy.paper_based.rwco_2020.p14

alias of enoppy.paper_based.rwco_2020.MultiProductBatchPlantProblem

enoppy.paper_based.rwco_2020.p15

alias of enoppy.paper_based.rwco_2020.WeightMinimizationSpeedReducerProblem

enoppy.paper_based.rwco_2020.p16

alias of enoppy.paper_based.rwco_2020.OptimalDesignIndustrialRefrigerationSystemProblem

enoppy.paper_based.rwco_2020.p17

alias of enoppy.paper_based.rwco_2020.TensionCompressionSpringDesignProblem

enoppy.paper_based.rwco_2020.p18

alias of enoppy.paper_based.rwco_2020.PressureVesselDesignProblem

enoppy.paper_based.rwco_2020.p19

alias of enoppy.paper_based.rwco_2020.WeldedBeamDesignProblem

enoppy.paper_based.rwco_2020.p2

alias of enoppy.paper_based.rwco_2020.HeatExchangerNetworkDesignCase2Problem

enoppy.paper_based.rwco_2020.p20

alias of enoppy.paper_based.rwco_2020.ThreeBarTrussDesignProblem

enoppy.paper_based.rwco_2020.p21

alias of enoppy.paper_based.rwco_2020.MultipleDiskClutchBrakeDesignProblem

enoppy.paper_based.rwco_2020.p22

alias of enoppy.paper_based.rwco_2020.PlanetaryGearTrainDesignOptimizationProblem

enoppy.paper_based.rwco_2020.p23

alias of enoppy.paper_based.rwco_2020.StepConePulleyProblem

enoppy.paper_based.rwco_2020.p3

alias of enoppy.paper_based.rwco_2020.HaverlyPoolingProblem

enoppy.paper_based.rwco_2020.p4

alias of enoppy.paper_based.rwco_2020.BlendingPoolingSeparationProblem

enoppy.paper_based.rwco_2020.p5

alias of enoppy.paper_based.rwco_2020.PropaneIsobutaneNButaneNonsharpSeparationProblem

enoppy.paper_based.rwco_2020.p6

alias of enoppy.paper_based.rwco_2020.OptimalOperationAlkylationUnitProblem

enoppy.paper_based.rwco_2020.p7

alias of enoppy.paper_based.rwco_2020.ReactorNetworkDesignProblem

enoppy.paper_based.rwco_2020.p8

alias of enoppy.paper_based.rwco_2020.ProcessSynthesis01Problem

enoppy.paper_based.rwco_2020.p9

alias of enoppy.paper_based.rwco_2020.ProcessSynthesisAndDesignProblem