dostring for #68

docs/src/api.md

@@ -106,6 +106,10 @@ Metaheuristics.compare
 Metaheuristics.gen_initial_state
 ```
 
+```@docs
+set_inital_solutions!
+```
+
 ## Variation 
 
 ```@docs

src/common/set_inital_solutions.jl

@@ -1,5 +1,42 @@
 include("gen_initial_state.jl")
 
+"""
+    set_inital_solutions!(optimizer, x, fx)
+
+Provide initial solutions to the `optimizer`.
+- `x` can be a `Vector` and `fx` a function or `fx = f(x)`
+- `x` can be a matrix containing solutions in rows.
+
+### Example
+
+```julia-repl
+julia> f(x) = abs(x[1]) + x[2]  + x[3]^2 # objective function
+f (generic function with 1 method)
+
+julia> algo  = ECA(N = 61); # optimizer
+
+julia> # one solution can be provided
+       x0 = [0.5, 0.5 0.5];
+
+julia> set_inital_solutions!(algo, x0, f);
+
+julia> # providing multiple solutions
+       X0 = rand(30, 3); # 30 solutions with dim 3
+
+julia> set_inital_solutions!(algo, X0, f);
+
+julia> optimize(f, [0 0 0; 1 1 1.0], algo)
++=========== RESULT ==========+
+  iteration: 413
+    minimum: 0
+  minimizer: [0.0, 0.0, 0.0]
+    f calls: 25132
+ total time: 0.0856 s
+stop reason: Small difference of objective function values.
++============================+
+```
+
+"""
 function set_inital_solutions!(algo::AbstractAlgorithm, solution::AbstractSolution)
     status = algo.status
     if !isnothing(status.best_sol) && !isempty(status.population)