Contributing

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

Algorithm Structure

When you call the optimize function, the following steps are carried out:

1. Initialization: status = initialize!(status, parameters, problem, information, options) this function should initialize a State with population members according to the parameters provided.
2. Main optimization loop: while status.stop == false do
   • update population, parameters via update_state!(status, parameters, problem, information, options), and
   • mainly set status.stop = stop_criteria(status, information, options)
3. When the loop in step 2 beaks, then a final function is called final_stage! in order to update or refine the final state, e.g., delete infeasible solutions in population, get non-dominated solutions, etc.

Initialization:

function initialize!(
                status, # an initiliazed State (if apply)
                parameters::AbstractParameters,
                problem,
                information,
                options,
                args...;
                kargs...
        )

    # initialize the stuff here
    return State(0.0, zeros(0)) # replace this
end

Optimization Process: In this step, the State is updated using the following function which is called at each iteration/generation.

function update_state!(
        status,
        parameters::AbstractParameters,
        problem,
        information,
        options,
        args...;
        kargs...
)
    # update any element in State 
    return
end

Final Step:

function final_stage(
        status,
        parameters::AbstractParameters,
        problem,
        information,
        options,
        args...;
        kargs...
)
    return
end

Parameters

Any proposed algorithm, let's say "XYZ", uses different parameters, then it is suggested to store them in a structure, e.g.:

# structure with algorithm parameters
mutable struct XYZ <: AbstractParameters
    N::Int # population size
    p_crossover::Float64 # crossover probability
    p_mutation::Float64 # mutation probability
end

# constructor like
function XYZ(;N = 0, p_crossover = 0.9, p_mutation = 0.1)
    parameters = XYZ(N, p_crossover, p_mutation)

    Algorithm(
        parameters,
        information = information,
        options = options,
    )
end

TODO Tutorial for creating and updating existent metaheuristics in this package.