Update README with more detailed specifications

README.md

@@ -5,7 +5,10 @@ Julia implementations of various animal-inspired optimizers.
 
 # Usage
 
-The design of `Zoolia` is inspired by various optimisation packages such as Optim.jl and BlackBoxOptim.jl, such that there is a main method, `optimize` which receives the function to be optimized, the search range, the number of dimensions, and the optimisation method.
+The design of `Zoolia` is inspired by various optimisation packages such as Optim.jl, BlackBoxOptim.jl, and Metaheuristics.jl. 
+
+There is a main method, `optimize` which receives the function to be optimized, the search range, the number of dimensions, and the optimisation method.
+
 ```julia
 using Zoolia
 
@@ -20,7 +23,38 @@ result = optimize(
     GreyWolfOptimizer()
 )
 ```
+
+## Result querying
+
+We can query the result of the optimisation with various accessor functions.
+```julia
+# WIP: To be expanded on
+best_fitness(result)
+best_position(result)
+```
+
+## Specifying range boundaries
+
+The `optimize` function will be able to accept a `search_range` argument input of various forms, depending on the `num_dimensions` specified.
+
+Generally, if the search range is fixed for all axes, the `search_range` should be a 2-length tuple/array specifying the minimum and maximum values for each dimension.
+
+If the search range is different for each dimension, the `search_range` should be an AbstractMatrix where each column corresponds to the minimum and maximum values of each variable. As a result, it is expected that the number of columns are equal to `num_dimensions`, and the number of rows is 2.
+
+```julia
+num_dimensions = 3
+search_range = (-5.0, 5.0)
+# min and max range for all 3 dimensions will be set to -5 to 5
+search_range = [-5 -1 -2; 5 1 2]
+# min and max range for x1, x2, x3 will be set to -5 .. 5, -1 .. 1, -2 .. 2 respectively.
+search_range = [-5 5; -1 1; -2 2]'
+# alternative notation to achieve the same result as above, note the ' at the end of the array
+```
+
+## Hyperparameter options
+
 One can also add hyperparameters for the method chosen in the optimizer method constructor.
+
 ```julia
 result = optimize(
     rosenbrock2d, 
@@ -32,24 +66,29 @@ result = optimize(
 )
 ```
 
-We can query the result of the optimisation with various accessor functions.
-```julia
-# WIP: To be expanded on
-best_fitness(result)
-```
+# Current implementations
 
-# Contributing
+- [x] [Grey Wolf optimization (2014)](https://www.sciencedirect.com/science/article/abs/pii/S0965997813001853)
+- [x] [Whale optimization algorithm (2016)](https://www.sciencedirect.com/science/article/abs/pii/S0965997816300163)
+
+# Implementing your own algorithm
 
 If you would like to implement your own animal optimizer, create a struct subtyping `Optimizer` and give it fields that specify the valid hyperparameters available for it.
 
 Then create a function whose name is of your optimizer type that receives arguments of the `func` to optimize, the `search_range`, and the `number_of_dimensions`.
 ```julia
 function (MyOpt::MyOptimizer)(func, search_range, n_dim)
     # Specify algorithm workings
-    # Return a OptimizationResult
+    # Then return an OptimizationResult
+    return OptimizationResult(
+        best_fitness,
+        best_position
+    )
 end
 ```
 
-Do keep to some convenience conventions for the user, such as 
+# Contributing
+
+Do keep to some convenience conventions for the user, such as:
 - creating default constructors with default hyperparameters 
 - sticking to naming conventions like ending the name in `Optimizer` as opposed to `Optimiser` or `Optimization`.