description texts updated

pybop/optimisers/_random_search.py

@@ -18,9 +18,8 @@ class RandomSearchImpl(PopulationBasedOptimiser):
 
     References:
     - The Random Search algorithm implemented in this work is based on principles outlined
-    in: 
-    Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control
-    - by Spall, J. C. (2003).
+    in "Introduction to Stochastic Search and Optimization: Estimation, Simulation, and
+    Control" by Spall, J. C. (2003).
     The implementation leverages the pints library framework, which provides tools for
     population-based optimization methods.
     """
@@ -30,7 +29,7 @@ def __init__(self, x0, sigma0=0.05, boundaries=None, population_size=None):
         self._dim = len(x0)  # Initialize _dim first
 
         super().__init__(x0, sigma0, boundaries=boundaries)
-        
+
         # Population size, defaulting to a suggested value
         self._population_size = population_size or self._suggested_population_size()
         self.step_size = self._sigma0
@@ -70,7 +69,7 @@ def tell(self, replies):
         """
         if not self._ready_for_tell:
             raise RuntimeError("Optimiser not ready for tell()")
-        
+
         self._iterations += 1
         self._ready_for_tell = False
 

pybop/optimisers/pints_optimisers.py

@@ -688,9 +688,8 @@ class RandomSearch(BasePintsOptimiser):
     """
     Adapter for the Random Search optimiser in PyBOP.
 
-    Random Search is a simple optimisation algorithm that samples parameter 
-    sets randomly within the given boundaries and identifies the best solution 
-    based on fitness.
+    Random Search is a simple optimisation algorithm that samples parameter sets randomly
+    within the given boundaries and identifies the best solution based on fitness.
 
     Parameters
     ----------