High performance metaheuristics for optimization purely coded in Julia.
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
julia> import Pkg; Pkg.add("Metaheuristics")
- ECA: Evolutionary Centers Algorithm
- DE: Differential Evolution
- PSO: Particle Swarm Optimization
- ABC: Artificial Bee Colony
- MOEA/D-DE: Multi-objective Evolutionary Algorithm based on Decomposition
- GSA: Gravitational Search Algorithm
- SA: Simulated Annealing
- WOA: Whale Optimization Algorithm
- 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
Assume you want to solve the following minimization problem.
Firstly, import the Metaheuristics package:
Code the objective function:
f(x) = 10length(x) + sum( x.^2 - 10cos.(2π*x) )
Instantiate the bounds, note that
bounds should be a $2\times 10$
the first row corresponds to the lower bounds whilst the second row corresponds to the
D = 10 bounds = [-5ones(D) 5ones(D)]'
Approximate the optimum using the function
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)
Please, be free to send me your PR, issue or any comment about this package for Julia.