diff --git a/lib/OptimizationNLopt/src/OptimizationNLopt.jl b/lib/OptimizationNLopt/src/OptimizationNLopt.jl
index a21cf262a..956903b19 100644
--- a/lib/OptimizationNLopt/src/OptimizationNLopt.jl
+++ b/lib/OptimizationNLopt/src/OptimizationNLopt.jl
@@ -75,7 +75,7 @@ function __map_optimizer_args!(cache::OptimizationCache, opt::NLopt.Opt;
 
         if !isnothing(local_options)
             for j in Dict(pairs(local_options))
-                eval(Meta.parse("NLopt." * string(j.first) * "!"))(local_meth, j.second)
+                NLopt.nlopt_set_param(opt, j.first, j.second)
             end
         end
 
@@ -93,7 +93,7 @@ function __map_optimizer_args!(cache::OptimizationCache, opt::NLopt.Opt;
     # add optimiser options from kwargs
     for j in kwargs
         if j.first != :cons_tol
-            eval(Meta.parse("NLopt." * string(j.first) * "!"))(opt, j.second)
+            NLopt.nlopt_set_param(opt, j.first, j.second)
         end
     end
 
diff --git a/lib/OptimizationNLopt/test/runtests.jl b/lib/OptimizationNLopt/test/runtests.jl
index 14c6a568e..de18f42fb 100644
--- a/lib/OptimizationNLopt/test/runtests.jl
+++ b/lib/OptimizationNLopt/test/runtests.jl
@@ -100,6 +100,29 @@ using Test, Random
         @test sol.retcode == ReturnCode.MaxTime
     end
 
+    @testset "dual_ftol_rel parameter" begin
+        # Test that dual_ftol_rel parameter can be passed to NLopt without errors
+        # This parameter is specific to MMA/CCSA algorithms for dual optimization tolerance
+        x0_test = zeros(2)
+        optprob = OptimizationFunction(rosenbrock, Optimization.AutoZygote())
+        prob = OptimizationProblem(optprob, x0_test, _p)
+        
+        # Test with NLopt.Opt interface
+        opt = NLopt.Opt(:LD_MMA, 2)
+        # This should not throw an error - the PR fixed the UndefVarError
+        sol = solve(prob, opt, dual_ftol_rel = 1e-6, maxiters = 100)
+        @test sol.retcode ∈ [ReturnCode.Success, ReturnCode.MaxIters]
+        
+        # Test with direct algorithm interface
+        sol = solve(prob, NLopt.LD_MMA(), dual_ftol_rel = 1e-5, maxiters = 100)
+        @test sol.retcode ∈ [ReturnCode.Success, ReturnCode.MaxIters]
+        
+        # Verify it works with other solver options
+        sol = solve(prob, NLopt.LD_MMA(), dual_ftol_rel = 1e-4, ftol_rel = 1e-6, 
+                   xtol_rel = 1e-6, maxiters = 100)
+        @test sol.retcode ∈ [ReturnCode.Success, ReturnCode.MaxIters]
+    end
+
     @testset "constrained" begin
         Random.seed!(1)
         cons = (res, x, p) -> res .= [x[1]^2 + x[2]^2 - 1.0]