A Survey on the Metaheuristics Applied to QAP for the Graphics Processing Units

Abstract

The computational power requirements of real-world optimization problems begin to exceed the general performance of the Central Processing Unit (CPU). The modeling of such problems is in constant evolution and requires more computational power. Solving them is expensive in computation time and even metaheuristics, well known for their eficiency, begin to be unsuitable for the increasing amount of data. Recently, thanks to the advent of languages such as CUDA, the development of parallel metaheuristics on Graphic Processing Unit (GPU) platform to solve combinatorial problems such as the Quadratic Assignment Problem (QAP) has received a growing interest. It is one of the most studied NP-hard problems and it is known for its high computational cost. In this paper, we survey several of the most important metaheuristics approaches for the QAP and we focus our survey on parallel metaheuristics using the GPU.

Quadratic assignment problem variants: A survey and an effective parallel memetic iterated tabu search

“…An adaptation using RoTS with roughly equal performance is presented by Novoa et al [81]. Although other QAP algorithms on GPU can be found (see a survey in Abdelkafi et al [3]), to the best of our knowledge these are the best performing ones.…”

A Parallel Tabu Search for the Large-scale Quadratic Assignment Problem

“…One of the natural way to tackle large-scale problem instances is to benefit from parallel computing facilities and to explore strategies to set up parallel solving procedures [4]. However, understanding how to fully take advantage form the aggregated computing power and what makes a parallel strategy successful is a difficult issue, in particular when tackling large-size problem instances.…”

Extension of PSO and ACO-PSO algorithms for solving Quadratic Assignment Problems

“…The goal in QAP is to position the items in the locations such a way that the sum of the product between flows and distances is minimal. Many practical problems like blackboard wiring [2], campus and hospital layout [3], typewriter keyboard design [4], scheduling [1] and many others [5][6][7] formulated as QAP's. For the given 'n' items and 'n' locations, two 'n × n' matrices 'A' and 'B' are given as an input to the algorithm.…”