Look at it this way: I am arguing against "No Free Lunch theorem says an optimization algorithm cannot solve all problems because for some problems it performs worse than other algorithms"; I am arguing approximate solutions are good enough, and in practice a wide variety of optimization algorithms find good enough solutions despite being worse than others algorithms for the problem class. Moreover, some algorithms/solvers can be configured, which fundamentally change the direction the solving takes (for example, a custom phase that uses your domain knowledge of the particular problem to get a good enough initial solution to be improved upon) (Side note: I am NOT affiliated with this post/project; from the website I don't really see a value add for it, especially since the site is lacking so many details).
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Look at it this way: I am arguing against "No Free Lunch theorem says an optimization algorithm cannot solve all problems because for some problems it performs worse than other algorithms"; I am arguing approximate solutions are good enough, and in practice a wide variety of optimization algorithms find good enough solutions despite being worse than others algorithms for the problem class. Moreover, some algorithms/solvers can be configured, which fundamentally change the direction the solving takes (for example, a custom phase that uses your domain knowledge of the particular problem to get a good enough initial solution to be improved upon) (Side note: I am NOT affiliated with this post/project; from the website I don't really see a value add for it, especially since the site is lacking so many details).
Approximate solutions are good enough, but approximate solutions are not part of the solution space for the kinds of problems covered by OP.