Algorithms

ECA(;
    η_max = 2.0,
    K = 7,
    N = 0,
    N_init = N,
    p_exploit = 0.95,
    p_bin = 0.02,
    p_cr = Float64[],
    adaptive = false,
    resize_population = false,
    information = Information(),
    options = Options()
)

Parameters for the metaheuristic ECA: step-size η_max,K is number of vectors to generate the center of mass, N is the population size.

Example

julia> f(x) = sum(x.^2)
f (generic function with 1 method)

julia> optimize(f, [-1 -1 -1; 1 1 1.0], ECA())
+=========== RESULT ==========+
  iteration: 1429
    minimum: 3.3152400000000004e-223
  minimizer: [4.213750597785841e-113, 5.290977430907081e-112, 2.231685329262638e-112]
    f calls: 29989
 total time: 0.1672 s
+============================+

julia> optimize(f, [-1 -1 -1; 1 1 1.0], ECA(N = 10, η_max = 1.0, K = 3))
+=========== RESULT ==========+
  iteration: 3000
    minimum: 0.000571319
  minimizer: [-0.00017150889316537758, -0.007955828028420616, 0.022538733289139145]
    f calls: 30000
 total time: 0.1334 s
+============================+

DE(;
    N  = 0,
    F  = 1.0,
    CR = 0.9,
    strategy = :rand1,
    information = Information(),
    options = Options()
)

Parameters for Differential Evolution (DE) algorithm: step-size F,CR controlls the binomial crossover, N is the population size. The parameter trategy is related to the variation operator (:rand1, :rand2, :best1, :best2, :randToBest1).

Example

julia> f(x) = sum(x.^2)
f (generic function with 1 method)

julia> optimize(f, [-1 -1 -1; 1 1 1.0], DE())
+=========== RESULT ==========+
  iteration: 1000
    minimum: 0
  minimizer: [0.0, 0.0, 0.0]
    f calls: 30000
 total time: 0.0437 s
+============================+

julia> optimize(f, [-1 -1 -1; 1 1 1.0], DE(N=50, F=1.5, CR=0.8))
+=========== RESULT ==========+
  iteration: 600
    minimum: 8.68798e-25
  minimizer: [3.2777877981303293e-13, 3.7650459509488005e-13, -7.871487597385812e-13]
    f calls: 30000
 total time: 0.0319 s
+============================+

PSO(;
    N  = 0,
    C1 = 2.0,
    C2 = 2.0,
    ω  = 0.8,
    information = Information(),
    options = Options()
)

Parameters for Particle Swarm Optimization (PSO) algorithm: learning rates C1 and C2, N is the population size and ω controlls the inertia weight.

Example

julia> f(x) = sum(x.^2)
f (generic function with 1 method)

julia> optimize(f, [-1 -1 -1; 1 1 1.0], PSO())
+=========== RESULT ==========+
  iteration: 1000
    minimum: 1.40522e-49
  minimizer: [3.0325415595139883e-25, 1.9862212295897505e-25, 9.543772256546461e-26]
    f calls: 30000
 total time: 0.1558 s
+============================+

julia> optimize(f, [-1 -1 -1; 1 1 1.0], PSO(N = 100, C1=1.5, C2=1.5, ω = 0.7))
+=========== RESULT ==========+
  iteration: 300
    minimum: 2.46164e-39
  minimizer: [-3.055334698085433e-20, -8.666986835846171e-21, -3.8118413472544027e-20]
    f calls: 30000
 total time: 0.1365 s
+============================+

ABC(;
    N = 50,
    Ne = div(N+1, 2),
    No = div(N+1, 2),
    limit=10,
    information = Information(),
    options = Options()
)

ABC implements the original parameters for the Artificial Bee Colony Algorithm. N is the population size, Ne is the number of employees, No is the number of outlookers bees. limit is related to the times that a solution is visited.

Example

julia> f(x) = sum(x.^2)
f (generic function with 1 method)

julia> optimize(f, [-1 -1 -1; 1 1 1.0], ABC())
+=========== RESULT ==========+
  iteration: 595
    minimum: 4.03152e-28
  minimizer: [1.489845115451046e-14, 1.2207275971717747e-14, -5.671872444705246e-15]
    f calls: 30020
 total time: 0.0360 s
+============================+

julia> optimize(f, [-1 -1 -1; 1 1 1.0], ABC(N = 80,  No = 20, Ne = 50, limit=5))
+=========== RESULT ==========+
  iteration: 407
    minimum: 8.94719e-08
  minimizer: [8.257485723496422e-5, 0.0002852795196258074, -3.5620824723352315e-5]
    f calls: 30039
 total time: 0.0432 s
+============================+

MOEAD_DE(weights)

MOEAD_DE implements the original version of MOEA/D-DE. It uses the contraint handling method based on the sum of violations (for constrained optimizaton): g(x, λ, z) = max(λ .* abs.(fx - z)) + sum(max.(0, gx)) + sum(abs.(hx))

To use MOEAD_DE, the output from the objective function should be a 3-touple (f::Vector, g::Vector, h::Vector), where f contains the objective functions, g and h are the equality and inequality constraints respectively.

A feasible solution is such that g_i(x) ≤ 0 and h_j(x) = 0.

Ref. Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II; Hui Li and Qingfu Zhang.

Example

Assume you want to solve the following optimizaton problem:

Minimize:

f(x) = (x_1, x_2)

subject to:

g(x) = x_1^2 + x_2^2 - 1 ≤ 0

x_1, x_2 ∈ [-1, 1]

A solution can be:

# Dimension
D = 2

# Objective function
f(x) = ( x, [sum(x.^2) - 1], [0.0] )

# bounds
bounds = [-1 -1;
           1  1.0
        ]

nobjectives = 2
npartitions = 100

# reference points (Das and Dennis's method)
weights = gen_ref_dirs(nobjectives, npartitions)

# define the parameters
moead_de = MOEAD_DE(weights, options=Options(debug=false, iterations = 250))

# optimize
status_moead = optimize(f, bounds, moead_de)

# show results
display(status_moead)

CGSA(;
    N::Int    = 30,
    chValueInitial::Real   = 20,
    chaosIndex::Real   = 9,
    ElitistCheck::Int    = 1,
    Rpower::Int    = 1,
    Rnorm::Int    = 2,
    wMax::Real   = chValueInitial,
    wMin::Real   = 1e-10,
    information = Information(),
    options = Options()
)

CGSA is an extension of the GSA algorithm but with Chaotic gravitational constants for the gravitational search algorithm.

Ref. Chaotic gravitational constants for the gravitational search algorithm. Applied Soft Computing 53 (2017): 407-419.

Parameters:

• N: Population size
• chValueInitial: Initial value for the chaos value
• chaosIndex: Integer 1 ≤ chaosIndex ≤ 10 is the function that model the chaos
• Rpower: power related to the distance norm(x)^Rpower
• Rnorm: is the value as in norm(x, Rnorm)

Example

julia> f(x) = sum(x.^2)
f (generic function with 1 method)

julia> optimize(f, [-1 -1 -1; 1 1 1.0], CGSA())
+=========== RESULT ==========+
  iteration: 500
    minimum: 8.63808e-08
  minimizer: [0.0002658098418993323, -1.140808975532608e-5, -0.00012488307670533095]
    f calls: 15000
 total time: 0.1556 s
+============================+

julia> optimize(f, [-1 -1 -1; 1 1 1.0], CGSA(N = 80, chaosIndex = 1))
+=========== RESULT ==========+
  iteration: 500
    minimum: 1.0153e-09
  minimizer: [-8.8507563788141e-6, -1.3050111801923072e-5, 2.7688577445980026e-5]
    f calls: 40000
 total time: 1.0323 s
+============================+

    SA(;
        x_initial::Vector = zeros(0),
        N::Int = 500,
        tol_fun::Real= 1e-4,
        information = Information(),
        options = Options()
    )

Parameters for the method of Simulated Annealing (Kirkpatrick et al., 1983).

Parameters:

• x_intial: Inital solution. If empty, then SA will generate a random one within the bounds.
• N: The number of test points per iteration.
• tol_fun: tolerance value for the Metropolis condition to accept or reject the test point as current point.

Example

julia> f(x) = sum(x.^2)
f (generic function with 1 method)

julia> optimize(f, [-1 -1 -1; 1 1 1.0], SA())
+=========== RESULT ==========+
  iteration: 60
    minimum: 5.0787e-68
  minimizer: [-2.2522059499734615e-34, 3.816133503985569e-36, 6.934348004465088e-36]
    f calls: 29002
 total time: 0.0943 s
+============================+

julia> optimize(f, [-1 -1 -1; 1 1 1.0], SA(N = 100, x_initial = [1, 0.5, -1]))
+=========== RESULT ==========+
  iteration: 300
    minimum: 1.99651e-69
  minimizer: [4.4638292404181215e-35, -1.738939846089388e-36, -9.542441152683457e-37]
    f calls: 29802
 total time: 0.0965 s
+============================+

WOA(;N = 30, information = Information(), options = Options())

Parameters for the Whale Optimization Algorithm. N is the population size (number of whales).

Example

julia> f(x) = sum(x.^2)
f (generic function with 1 method)

julia> optimize(f, [-1 -1 -1; 1 1 1.0], WOA())
+=========== RESULT ==========+
  iteration: 500
    minimum: 3.9154600000000003e-100
  minimizer: [8.96670478694908e-52, -1.9291317455298046e-50, 4.3113080446722046e-51]
    f calls: 15000
 total time: 0.0134 s
+============================+

julia> optimize(f, [-1 -1 -1; 1 1 1.0], WOA(N = 100))
+=========== RESULT ==========+
  iteration: 500
    minimum: 1.41908e-145
  minimizer: [9.236161414012512e-74, -3.634919950380001e-73, 3.536831799149254e-74]
    f calls: 50000
 total time: 0.0588 s
+============================+

function NSGA2(;
    N = 100,
    η_cr = 20,
    p_cr = 0.9,
    η_m = 20,
    p_m = 1.0 / D,
    ε = eps(),
    information = Information(),
    options = Options(),
)

Parameters for the metaheuristic NSGA-II.

Parameters:

• N Population size.
• η_cr η for the crossover.
• p_cr Crossover probability.
• η_m η for the mutation operator.
• p_m Mutation probability (1/D for D-dimensional problem by default).

To use NSGA2, the output from the objective function should be a 3-touple (f::Vector, g::Vector, h::Vector), where f contains the objective functions, g and h are inequality, equality constraints respectively.

A feasible solution is such that g_i(x) ≤ 0 and h_j(x) = 0.

using Metaheuristics

# Dimension
D = 2

# Objective function
f(x) = ( x, [sum(x.^2) - 1], [0.0] ) 

# bounds
bounds = [-1 -1;
           1  1.0
        ]

# define the parameters (use `NSGA2()` for using default parameters)
nsga2 = NSGA2(N = 100, p_cr = 0.85)

# optimize
status = optimize(f, bounds, nsga2)

# show results
display(status)

function NSGA3(;
    N = 100,
    η_cr = 20,
    p_cr = 0.9,
    η_m = 20,
    p_m = 1.0 / D,
    ε = eps(),
    information = Information(),
    options = Options(),
)

Parameters for the metaheuristic NSGA-III.

Parameters:

• N Population size.
• η_cr η for the crossover.
• p_cr Crossover probability.
• η_m η for the mutation operator.
• p_m Mutation probability (1/D for D-dimensional problem by default).

To use NSGA3, the output from the objective function should be a 3-touple (f::Vector, g::Vector, h::Vector), where f contains the objective functions, g and h are inequality, equality constraints respectively.

A feasible solution is such that g_i(x) ≤ 0 and h_j(x) = 0.

using Metaheuristics


# Objective function, bounds, and the True Pareto front
f, bounds, pf = Metaheuristics.TestProblems.get_problem(:DTLZ2)


# define the parameters (use `NSGA3()` for using default parameters)
nsga3 = NSGA3(p_cr = 0.9)

# optimize
status = optimize(f, bounds, nsga3)

# show results
display(status)