A new primal heuristic was recently proposed in The granularity concept in mixed-integer optimization. The idea is to define a subset of the feasible set so it is are guaranteed that at-least one rounding exists that is in the feasible set, optimize this relaxation and then round once to get to a feasible solution faster. This is trivial for Linear constraints but doing it for non-linear constraints might be harder or require an iterative approach, (i haven't done the math). I also suggested this heuristic for Juniper.
A new primal heuristic was recently proposed in The granularity concept in mixed-integer optimization. The idea is to define a subset of the feasible set so it is are guaranteed that at-least one rounding exists that is in the feasible set, optimize this relaxation and then round once to get to a feasible solution faster. This is trivial for Linear constraints but doing it for non-linear constraints might be harder or require an iterative approach, (i haven't done the math). I also suggested this heuristic for Juniper.