An Improved Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem

Abstract

In this paper, an improved hybrid genetic-hierarchical algorithm for the solution of the quadratic assignment problem (QAP) is presented. The algorithm is based on the genetic search combined with the hierarchical (hierarchicity-based multi-level) iterated tabu search procedure. The following are two main scientific contributions of the paper: (i) the enhanced two-level hybrid primary (master)-secondary (slave) genetic algorithm is proposed; (ii) the augmented universalized multi-strategy perturbation (mutation process)—which is integrated within a multi-level hierarchical iterated tabu search algorithm—is implemented. The proposed scheme enables efficient balance between intensification and diversification in the search process. The computational experiments have been conducted using QAP instances of sizes up to 729. The results from the experiments with the improved algorithm demonstrate the outstanding performance of the new proposed approach. This is especially obvious for the small- and medium-sized instances. Nearly 90% of the runs resulted in (pseudo-)optimal solutions. Three new best-known solutions have been achieved for very hard, challenging QAP instances.