High performance metaheuristics for global optimization.

Open the Julia (Julia 1.1 or later) REPL and press ] to open the Pkg prompt. To add this package, use the add command:

pkg> add Metaheuristics

Or, equivalently, via the Pkg API:

julia> import Pkg; Pkg.add("Metaheuristics")

Some representative metaheuristics are developed here, including those for single- and multi-objective optimization. Moreover, some constraint handling techniques have been considered in most of the implemented algorithms.

• ECA: Evolutionary Centers Algorithm
• DE: Differential Evolution
• PSO: Particle Swarm Optimization
• ABC: Artificial Bee Colony
• GSA: Gravitational Search Algorithm
• SA: Simulated Annealing
• WOA: Whale Optimization Algorithm
• MCCGA: Machine-coded Compact Genetic Algorithm
• GA: Genetic Algorithm

• MOEA/D-DE: Multi-objective Evolutionary Algorithm based on Decomposition
• NSGA-II: A fast and elitist multi-objective genetic algorithm: NSGA-II
• NSGA-III: Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach
• SMS-EMOA: An EMO algorithm using the hypervolume measure as selection criterion
• SPEA2: Improved Strength Pareto Evolutionary Algorithm
• CCMO: Coevolutionary Framework for Constrained Multiobjective Optimization

• GD: Generational Distance
• IGD, IGD+: Inverted Generational Distance (Plus)
• C-metric: Covering Indicator
• HV: Hypervolume
• Δₚ (Delta p): Averaged Hausdorff distance
• Spacing Indicator
• and more...

Assume you want to solve the following minimization problem.

Minimize:

where , i.e., for . D is the dimension number, assume D=10.

Firstly, import the Metaheuristics package:

using Metaheuristics

Code the objective function:

f(x) = 10length(x) + sum( x.^2 - 10cos.(2π*x)  )

Instantiate the bounds, note that bounds should be a $2\times 10$ Matrix where the first row corresponds to the lower bounds whilst the second row corresponds to the upper bounds.

D = 10
bounds = [-5ones(D) 5ones(D)]'

Approximate the optimum using the function optimize.

result = optimize(f, bounds)

Optimize returns a State datatype which contains some information about the approximation. For instance, you may use mainly two functions to obtain such approximation.

@show minimum(result)
@show minimizer(result)

See the documentation for more details, examples and options.

Please, be free to send me your PR, issue or any comment about this package for Julia.