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

TL;DR

This paper presents a parallel random search algorithm for large-scale constrained pseudo-Boolean optimization problems, leveraging shared memory and cluster computing to improve efficiency and solution quality.

Contribution

It introduces a parallelized version of the probability changing method with adaptation and rollback, optimized for high-performance computing environments.

Findings

Achieves near-ideal speed-up on large-scale problems

Effective in reducing computational resources for constrained optimization

Demonstrates high parallel efficiency in shared memory and cluster systems

Abstract

In this paper, we consider an approach to the parallelizing of the algorithms realizing the modified probability changigng method with adaptation and partial rollback 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-scale non-linear pseudo-Boolean optimization problems with linear constraints. Initially designed for unconstrained optimization, the probability changing method (MIVER) allows us finding the approximate solution of different linear and non-linear pseudo-Boolean optimization problems with constraints. Although, in case of large-scale problems, the computational demands are also very high and the precision of the result depends on the time spent. In case of the constrained optimization problem, even the search of any permissibly solution can take very large computational resources. The rapid development of the parallel processor systems which are often implemented even in the computer systems designed for home use allows to reduce significantly the time spent to find the acceptable solution with a speed-up close to ideal.