Can an optimization algorithm be "universal"? I am wondering if a Bayesian Optimization framework (e.g. Google's Vizier) can be used in lieu of a traditional solver like Gurobi or CPLEX. 

In trying to answer this question, I realized that I don't know enough about optimization theory to answer a more general question: Just how interchangeable are heuristic approaches for solving computationally hard optimization problems? 

Coming from an ML background, I'm reasoning by analogy with supervised learning problems: In ML, some methods like Neural Networks (with a sufficient number of layers) or Support Vector Machines are universal, in that they can approximate any shape decision boundary or regression function up to an arbitrary level of precision. 

Are there equivalent algorithms in optimization theory that can be used to solve any optimization problem (linear, non-linear, continuous, discrete, etc...), e.g. can Genetic Algorithms or Particle Swarm Optimization be thrown at any optimization problem and give us a reasonable solution? SGD is used to solve NP-Complete problems (i.e. training a neural network) - does that mean that it can be used for any optimization problem? 

I assume that the reverse is true: Not all optimization are universal, for example methods that work for LP or QP don't necessarily work for harder problems. 

If it is indeed the case that some optimization algorithms are universal, is Bayesian Optimization one of these universal algorithms? Can be used to approach LP, QP, MIP, TSP, and NP-Hard problems in general?
There is no heuristic that is "universal" in practice.  Which heuristic works best in practice often depends on the specific problem you're dealing with.  There's no one "silver bullet" heuristic that works great on every optimization problem.