diff --git a/docs/Project.toml b/docs/Project.toml
index b12220dc8..f6ccf5451 100644
--- a/docs/Project.toml
+++ b/docs/Project.toml
@@ -130,6 +130,9 @@ path = "../lib/OptimizationPyCMA"
 [sources.OptimizationQuadDIRECT]
 path = "../lib/OptimizationQuadDIRECT"
 
+[sources.QuadDIRECT]
+url = "https://github.com/timholy/QuadDIRECT.jl.git"
+
 [sources.OptimizationSciPy]
 path = "../lib/OptimizationSciPy"
 
diff --git a/docs/src/optimization_packages/evolutionary.md b/docs/src/optimization_packages/evolutionary.md
index 6be2e1621..98edfc327 100644
--- a/docs/src/optimization_packages/evolutionary.md
+++ b/docs/src/optimization_packages/evolutionary.md
@@ -1,6 +1,6 @@
 # Evolutionary.jl
 
-[`Evolutionary`](https://github.com/wildart/Evolutionary.jl) is a Julia package implementing various evolutionary and genetic algorithm.
+[`Evolutionary`](https://github.com/SciML/Evolutionary.jl) is a Julia package implementing various evolutionary and genetic algorithm.
 
 ## Installation: OptimizationEvolutionary.jl
 
@@ -15,16 +15,16 @@ Pkg.add("OptimizationEvolutionary");
 
 ### Without Constraint Equations
 
-The methods in [`Evolutionary`](https://github.com/wildart/Evolutionary.jl) are performing global optimization on problems without
+The methods in [`Evolutionary`](https://github.com/SciML/Evolutionary.jl) are performing global optimization on problems without
 constraint equations. These methods work both with and without lower and upper constraints set by `lb` and `ub` in the `OptimizationProblem`.
 
 A `Evolutionary` algorithm is called by one of the following:
 
-  - [`Evolutionary.GA()`](https://wildart.github.io/Evolutionary.jl/stable/ga/): **Genetic Algorithm optimizer**
+  - [`Evolutionary.GA()`](https://sciml.github.io/Evolutionary.jl/stable/ga/): **Genetic Algorithm optimizer**
 
-  - [`Evolutionary.DE()`](https://wildart.github.io/Evolutionary.jl/stable/de/): **Differential Evolution optimizer**
-  - [`Evolutionary.ES()`](https://wildart.github.io/Evolutionary.jl/stable/es/): **Evolution Strategy algorithm**
-  - [`Evolutionary.CMAES()`](https://wildart.github.io/Evolutionary.jl/stable/cmaes/): **Covariance Matrix Adaptation Evolution Strategy algorithm**
+  - [`Evolutionary.DE()`](https://sciml.github.io/Evolutionary.jl/stable/de/): **Differential Evolution optimizer**
+  - [`Evolutionary.ES()`](https://sciml.github.io/Evolutionary.jl/stable/es/): **Evolution Strategy algorithm**
+  - [`Evolutionary.CMAES()`](https://sciml.github.io/Evolutionary.jl/stable/cmaes/): **Covariance Matrix Adaptation Evolution Strategy algorithm**
 
 Algorithm-specific options are defined as `kwargs`. See the respective documentation for more detail.
 
diff --git a/docs/src/optimization_packages/nlopt.md b/docs/src/optimization_packages/nlopt.md
index b2d22886c..0ab505542 100644
--- a/docs/src/optimization_packages/nlopt.md
+++ b/docs/src/optimization_packages/nlopt.md
@@ -1,6 +1,6 @@
 # NLopt.jl
 
-[`NLopt`](https://github.com/jump-dev/NLopt.jl) is Julia package interfacing to the free/open-source [`NLopt library`](http://ab-initio.mit.edu/nlopt/) which implements many optimization methods both global and local [`NLopt Documentation`](https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/).
+[`NLopt`](https://github.com/jump-dev/NLopt.jl) is Julia package interfacing to the free/open-source [`NLopt library`](https://nlopt.readthedocs.io/en/latest/) which implements many optimization methods both global and local [`NLopt Documentation`](https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/).
 
 ## Installation: OptimizationNLopt.jl
 
diff --git a/lib/SimpleOptimization/src/SimpleOptimization.jl b/lib/SimpleOptimization/src/SimpleOptimization.jl
index 2c88ffa2a..b1046a2e5 100644
--- a/lib/SimpleOptimization/src/SimpleOptimization.jl
+++ b/lib/SimpleOptimization/src/SimpleOptimization.jl
@@ -181,7 +181,7 @@ function SciMLBase.__solve(cache::OptimizationCache{O}) where {O <: SimpleLBFGS}
         nlprob,
         SimpleLimitedMemoryBroyden(;
             threshold = __get_threshold(cache.opt),
-            linesearch = Val(false)
+            linesearch = nothing
         );
         maxiters = maxiters,
         abstol = abstol,
@@ -222,7 +222,7 @@ function SciMLBase.__solve(cache::OptimizationCache{O}) where {O <: SimpleBFGS}
     nlprob = NonlinearProblem(∇f, cache.u0)
     nlsol = solve(
         nlprob,
-        SimpleBroyden(; linesearch = Val(false));
+        SimpleBroyden(; linesearch = nothing);
         maxiters = maxiters,
         abstol = abstol,
         reltol = reltol