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The nonlinear constrained optimization interface in Evolutinary assumes that the user can write the optimization problem as follows:

\[\min_{x\in\mathbb{R}^n} f(x) \quad \text{such that}\\ l_x \leq \phantom{c(}x\phantom{)} \leq u_x \\ l_c \leq c(x) \leq u_c.\]

For equality constraints on $x_j$ or $c(x)_j$ you set those particular entries of bounds to be equal, $l_j=u_j$. Likewise, setting $l_j=-\infty$ or $u_j=\infty$ means that the constraint is unbounded from below or above respectively.

Following examples show how to use constraints to optimize the Booth function. The function is defined as follows:

julia> f(x)=(x[1]+2x[2]-7)^2+(2x[1]+x[2]-5)^2 # Booth
f (generic function with 1 method)

The function is usually evaluated on the square $x_i ∈ [-10, 10]$, for all $i = 1, 2$. The global minimum on this function is located at $(1,3)$.

ga = GA(populationSize=100,selection=uniformranking(3),
        mutation=gaussian(),crossover=uniformbin())
x0 = [0., 0.]
results = Evolutionary.optimize(f, x0, ga)
 * Status: success

 * Candidate solution
    Minimizer:  [0.7925695998298106, 3.180850457362758]
    Minimum:    0.0785602322816691
    Iterations: 24

 * Found with
    Algorithm: GA[P=100,x=0.8,μ=0.1,ɛ=0]

 * Convergence measures
    |f(x) - f(x')| = 0.0 ≤ 1.0e-12

 * Work counters
    Seconds run:   0.0223 (vs limit Inf)
    Iterations:    24
    f(x) calls:    2500

Box Constrained Optimization

We want to optimize the Booth function in the box $0.5 \leq x_i \leq 2.0$, starting from the point $x_0=(1,1)$.

Reusing our Booth example from above, boxed minimization is performed by providing vectors of lower and upper bounds as follows:

lower = [0.5, 0.5]
upper = [2.0, 2.0]
x0 = [1., 1.]
results = Evolutionary.optimize(f, BoxConstraints(lower, upper), x0, ga)
 * Status: success

 * Candidate solution
    Minimizer:  [1.8129031194565053, 2.0]
    Minimum:    1.8008324524585442
    Iterations: 21

 * Found with
    Algorithm: GA[P=100,x=0.8,μ=0.1,ɛ=0]

 * Convergence measures
    |f(x) - f(x')| = 0.0 ≤ 1.0e-12

 * Work counters
    Seconds run:   0.0004 (vs limit Inf)
    Iterations:    21
    f(x) calls:    2200

The box constraints can be also defined using BoxConstraints object.

Evolutionary.BoxConstraints — Type

This type encodes box constraints for the optimization function parameters.

The constructor takes following arguments:

  • lower is the vector of value lower bounds
  • upper is the vector of value upper bounds

Note: Sizes of the lower and upper bound vectors must be equal.

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cnst = BoxConstraints([0.5, 0.5], [2.0, 2.0])
Box Constraints:
    x[1]≥0.5, x[1]≤2.0, x[2]≥0.5, x[2]≤2.0

This object can be passed as a parameter to the optimization call, Evolutionary.optimize:

results = Evolutionary.optimize(f, cnst, x0, ga) # or Evolutionary.optimize(f, cnst, ga)
 * Status: success

 * Candidate solution
    Minimizer:  [1.8233787092919858, 2.0]
    Minimum:    1.802732820240796
    Iterations: 26

 * Found with
    Algorithm: GA[P=100,x=0.8,μ=0.1,ɛ=0]

 * Convergence measures
    |f(x) - f(x')| = 0.0 ≤ 1.0e-12

 * Work counters
    Seconds run:   0.0005 (vs limit Inf)
    Iterations:    26
    f(x) calls:    2700

Penalty Constrained Optimization

For the penalty constrained optimization, any value and linear/nonlinear constraints are transformed into the penalty to the minimized fitness function. In order to provide linear/nonlinear constraints to an optimization problem, you can use the following penalty constraint algorithm:

Evolutionary.PenaltyConstraints — Type

This type encodes constraints as the following additional penalty for an objective function:

$p(x) = \sum^n_{i=1} r_i max(0, g_i(x))^2$

where $r_i$ is a penalty value for $i$th constraint, and $g_i(x)$ is an inequality constraint. The equality constraints $h_i(x) = 0$ are transformed to inequality constraints as follows:

$h(x) - \epsilon \leq 0$

The constructor takes following arguments:

  • penalties: a vector of penalty values per constraint (optional)
  • lx: a vector of value lower bounds
  • ux: a vector of value upper bounds
  • lc: a vector of constrain function lower bounds
  • uc: a vector of constrain function upper bounds
  • c: a constraint function which returns a constrain values
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We want to minimize the following function $f(x,y) = 3x+9y$ that is subject to constraints $\sqrt(xy) \geq 100$ and $x,y \geq 0$. The minimum of this function is near $(173.41, 57.8)$. We begin by defining constraints as follows:

# x, y ≥ 0
lx = [0.0, 0.0] # lower bound for values
ux = [Inf, Inf] # upper bound for values
# √xy ≥ 100
c(x) = [ prod(map(e->sqrt(e>0 ? e : 0.0), x)) ] # negative values are zeroed
lc   = [100.0] # lower bound for constraint function
uc   = [ Inf ]   # upper bound for constraint function
con = PenaltyConstraints(100.0, lx, ux, lc, uc, c) # first parameter is a penalty value
Penalty Constraints:
  Penalties: [100.0, 100.0, 100.0]
ConstraintBounds:
  Variables:
    x[1]≥0.0, x[2]≥0.0
  Linear/nonlinear constraints:
    c_1≥100.0

Now, we define the fitness function, an initial individual structure, and algorithm parameters; then we perform minimization as follows:

f(x) = 3x[1]+9x[2] # fitness function
x0 = [1., 1.] # individual
ga = GA(populationSize=100,selection=tournament(7),
        mutation=gaussian(),crossover=intermediate(2))
results = Evolutionary.optimize(f, con, x0, ga)
 * Status: success

 * Candidate solution
    Minimizer:  [172.9716290671532, 57.752917943828706]
    Minimum:    1038.9608417924103
    Iterations: 97

 * Found with
    Algorithm: GA[P=100,x=0.8,μ=0.1,ɛ=0]

 * Convergence measures
    |f(x) - f(x')| = 0.0 ≤ 1.0e-12

 * Work counters
    Seconds run:   0.0058 (vs limit Inf)
    Iterations:    97
    f(x) calls:    9800

We can use worst fitness constraint algorithm which doesn't require to specify the constraint penalty value

Evolutionary.WorstFitnessConstraints — Type

This type encodes constraints as the following additional penalty for an objective function:

$p(x) = f_{worst} + \sum^n_{i=1} |g_i(x)|$

if $x$ is not feasible, otherwise no penalty is applied.

The constructor takes following arguments:

  • lx: a vector of value lower bounds
  • ux: a vector of value upper bounds
  • lc: a vector of constrain function lower bounds
  • uc: a vector of constrain function upper bounds
  • c: a constraint function which returns a constrain values
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con = WorstFitnessConstraints(lx, ux, lc, uc, c)
Worst Fitness ConstraintBounds:
  Variables:
    x[1]≥0.0, x[2]≥0.0
  Linear/nonlinear constraints:
    c_1≥100.0
results = Evolutionary.optimize(f, con, x0, ga)
 * Status: success

 * Candidate solution
    Minimizer:  [168.10650008313067, 59.48609998675682]
    Minimum:    1039.6944001302036
    Iterations: 57

 * Found with
    Algorithm: GA[P=100,x=0.8,μ=0.1,ɛ=0]

 * Convergence measures
    |f(x) - f(x')| = 0.0 ≤ 1.0e-12

 * Work counters
    Seconds run:   0.0131 (vs limit Inf)
    Iterations:    57
    f(x) calls:    5800

Auxiliary Functions

NLSolversBase.value — Method
value(c::AbstractConstraints, x)

Return a values of constraints for a variable x given the set of constraints in the object c.

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Evolutionary.isfeasible — Function
isfeasible(bounds::ConstraintBounds, x) -> Bool

Return true if point x is feasible, given the bounds object with bounds lx, ux, lc, and uc. x is feasible if

lx[i] <= x[i] <= ux[i]
lc[i] <= c(x)[i] <= uc[i]

for all possible i.

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isfeasible(c::AbstractConstraints, x) -> Bool

Return true if point x is feasible, given the constraints object c.

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Evolutionary.penalty — Function
penalty(constraints, x)

Calculate a penalty for the variable x given the set of constraints.

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Evolutionary.penalty! — Function
penalty!(fitness, constraints, population)

Set a penalty to the fitness given the set of constraints and population.

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Evolutionary.apply! — Function
apply!(c::AbstractConstraints, x)

Appliy constrains c to a variable x, and return the variable.

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