Sounds like you have a good solution for your problem. Whatever solves the problem and is already done is naturally a good solution!
From my experience, the most important thing to get right in any local search algorithm is the state representation and the moves generated. If those work well, then any combination of metheuristics like simulated annealing, tabu search, population based search, restarts, parallel exploration, portfolio methods, and so on. Quite often the literature focuses only on the meta heuristic, but not so much on the representation, which IMHO can be an issue.
As an example, for some problems that I have solved with local search, the most impact in improving performance of the algorithms have been in improving the base. Making moves and score updates fast, and making the moves generated meaningful in the context.
From my experience, the most important thing to get right in any local search algorithm is the state representation and the moves generated. If those work well, then any combination of metheuristics like simulated annealing, tabu search, population based search, restarts, parallel exploration, portfolio methods, and so on. Quite often the literature focuses only on the meta heuristic, but not so much on the representation, which IMHO can be an issue.
As an example, for some problems that I have solved with local search, the most impact in improving performance of the algorithms have been in improving the base. Making moves and score updates fast, and making the moves generated meaningful in the context.