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In this paper, we consider an approach to the parallelizing of the algorithmsrealizing the modified probability changigng method with adaptation and partialrollback procedure for constrained pseudo-Boolean optimization problems.Existing optimization algorithms are adapted for the shared memory and clusters(PVM library). The parallel efficiency is estimated for the lagre-scalenon-linear pseudo-Boolean optimization problems with linear constraints.Initially designed for unconstrained optimization, the probability changingmethod (MIVER) allows us finding the approximate solution of different linearand non-linear pseudo-Boolean optimization problems with constraints. Although,in case of large-scale problems, the computational demands are also very highand the precision of the result depends on the time spent. In case of theconstrained optimization problem, even the search of any permissibly solutioncan take very large computational resources. The rapid development of theparallel processor systems which are often implemented even in the computersystems designed for home use allows to reduce significantly the time spent tofind the acceptable solution with a speed-up close to ideal.

Parallel Random Search Algorithm of Constrained Pseudo-Boolean Optimization for Some Distinctive Large-Scale Problems