diff --git a/src/function.jl b/src/function.jl
index 8f9dc46..bbf41ec 100644
--- a/src/function.jl
+++ b/src/function.jl
@@ -43,6 +43,71 @@ function that is not defined, an error is thrown.
 For more information on the use of automatic differentiation, see the
 documentation of the `AbstractADType` types.
 """
+
+
+function instantiate_function(f::MultiObjectiveOptimizationFunction, x, ::SciMLBase.NoAD,
+        p, num_cons = 0)
+    jac = f.jac === nothing ? nothing : (J, x, args...) -> f.jac(J, x, p, args...)
+    hess = f.hess === nothing ? nothing : [(H, x, args...) -> h(H, x, p, args...) for h in f.hess]
+    hv = f.hv === nothing ? nothing : (H, x, v, args...) -> f.hv(H, x, v, p, args...)
+    cons = f.cons === nothing ? nothing : (res, x) -> f.cons(res, x, p)
+    cons_j = f.cons_j === nothing ? nothing : (res, x) -> f.cons_j(res, x, p)
+    cons_jvp = f.cons_jvp === nothing ? nothing : (res, x) -> f.cons_jvp(res, x, p)
+    cons_vjp = f.cons_vjp === nothing ? nothing : (res, x) -> f.cons_vjp(res, x, p)
+    cons_h = f.cons_h === nothing ? nothing : (res, x) -> f.cons_h(res, x, p)
+    hess_prototype = f.hess_prototype === nothing ? nothing :
+                     convert.(eltype(x), f.hess_prototype)
+    cons_jac_prototype = f.cons_jac_prototype === nothing ? nothing :
+                         convert.(eltype(x), f.cons_jac_prototype)
+    cons_hess_prototype = f.cons_hess_prototype === nothing ? nothing :
+                          [convert.(eltype(x), f.cons_hess_prototype[i])
+                           for i in 1:num_cons]
+    expr = symbolify(f.expr)
+    cons_expr = symbolify.(f.cons_expr)
+
+    return MultiObjectiveOptimizationFunction{true}(f.f, SciMLBase.NoAD(); jac = jac, hess = hess,
+        hv = hv,
+        cons = cons, cons_j = cons_j, cons_jvp = cons_jvp, cons_vjp = cons_vjp, cons_h = cons_h,
+        hess_prototype = hess_prototype,
+        cons_jac_prototype = cons_jac_prototype,
+        cons_hess_prototype = cons_hess_prototype,
+        expr = expr, cons_expr = cons_expr,
+        sys = f.sys,
+        observed = f.observed)
+end
+
+function instantiate_function(f::MultiObjectiveOptimizationFunction, cache::ReInitCache, ::SciMLBase.NoAD,
+        num_cons = 0)
+    jac = f.jac === nothing ? nothing : (J, x, args...) -> f.jac(J, x, cache.p, args...)
+    hess = f.hess === nothing ? nothing : [(H, x, args...) -> h(H, x, cache.p, args...) for h in f.hess]
+    hv = f.hv === nothing ? nothing : (H, x, v, args...) -> f.hv(H, x, v, cache.p, args...)
+    cons = f.cons === nothing ? nothing : (res, x) -> f.cons(res, x, cache.p)
+    cons_j = f.cons_j === nothing ? nothing : (res, x) -> f.cons_j(res, x, cache.p)
+    cons_jvp = f.cons_jvp === nothing ? nothing : (res, x) -> f.cons_jvp(res, x, cache.p)
+    cons_vjp = f.cons_vjp === nothing ? nothing : (res, x) -> f.cons_vjp(res, x, cache.p)
+    cons_h = f.cons_h === nothing ? nothing : (res, x) -> f.cons_h(res, x, cache.p)
+    hess_prototype = f.hess_prototype === nothing ? nothing :
+                     convert.(eltype(cache.u0), f.hess_prototype)
+    cons_jac_prototype = f.cons_jac_prototype === nothing ? nothing :
+                         convert.(eltype(cache.u0), f.cons_jac_prototype)
+    cons_hess_prototype = f.cons_hess_prototype === nothing ? nothing :
+                          [convert.(eltype(cache.u0), f.cons_hess_prototype[i])
+                           for i in 1:num_cons]
+    expr = symbolify(f.expr)
+    cons_expr = symbolify.(f.cons_expr)
+
+    return MultiObjectiveOptimizationFunction{true}(f.f, SciMLBase.NoAD(); jac = jac, hess = hess,
+        hv = hv,
+        cons = cons, cons_j = cons_j, cons_jvp = cons_jvp, cons_vjp = cons_vjp, cons_h = cons_h,
+        hess_prototype = hess_prototype,
+        cons_jac_prototype = cons_jac_prototype,
+        cons_hess_prototype = cons_hess_prototype,
+        expr = expr, cons_expr = cons_expr,
+        sys = f.sys,
+        observed = f.observed)
+end
+
+
 function instantiate_function(f, x, ::SciMLBase.NoAD,
         p, num_cons = 0)
     grad = f.grad === nothing ? nothing : (G, x, args...) -> f.grad(G, x, p, args...)
@@ -113,3 +178,5 @@ function instantiate_function(f, x, adtype::ADTypes.AbstractADType,
     adpkg = adtypestr[strtind:(open_brkt_ind - 1)]
     throw(ArgumentError("The passed automatic differentiation backend choice is not available. Please load the corresponding AD package $adpkg."))
 end
+
+