We extend wrapper compatibility of the Random Search, Grid Search, Genetic, HyperOpt, and GPy-Opt 111 planners to encompass discrete, categorical, and mixed-parameter types. We also added wrappers for 5 Bayesian optimization strategies, each compatible with all parameter types. The new algorithms are Gryffin, 67 SMAC, Dragonfly, 117 HEBO, and BoTorch. Gryffin 67 is an extension of the Phoenics 115 algorithm, which employs a kernel regression surrogate model, and can be applied to mixed continuouscategorical parameter spaces. SMAC 116 is a Bayesian optimization package that uses random forests as the surrogate model. Dragonfly is an open source Python library for scalable and robust Bayesian optimization using Gaussian process (GP) surrogate models. Notably, Dragonfly allows for optimization over high-dimensional domains, optimization over structured combinatorial spaces and methods for handling parallel evaluations. The Dragonfly wrapper in Olympus does not currently support multi-fidelity optimization, parallel evaluations, or constrained optimization. Grid-like ✓ ✓ ✓ ✓ ✓ × Latin Hypercube Grid-like