A Multi-Reference Relaxation Enforced Neighborhood Search Heuristic in SCIP

TL;DR

This paper introduces a Multi-Reference Relaxation Enforced Neighborhood Search heuristic in SCIP, demonstrating its effectiveness in improving solution quality for MILP problems by leveraging multiple reference solutions.

Contribution

It is the first to integrate and evaluate a multi-reference heuristic within a full MILP solver, coupled with a new reference solution generation method.

Findings

MRENS improves solution quality over single-reference methods.

The heuristic enhances SCIP's primal solution capabilities.

Experimental results on MIPLIB 2017 validate its effectiveness.

Abstract

This paper proposes and evaluates a Multi-Reference Relaxation Enforced Neighborhood Search (MRENS) heuristic within the SCIP solver. This study marks the first integration and evaluation of MRENS in a full-fledged MILP solver, specifically coupled with the recently-introduced Lagromory separator for generating multiple reference solutions. Computational experiments on the MIPLIB 2017 benchmark set show that MRENS, with multiple reference solutions, improves the solver's ability to find higher-quality feasible solutions compared to single-reference approaches. This study highlights the potential of multi-reference heuristics in enhancing primal heuristics in MILP solvers.