An efficient implementation of the simulated annealing heuristic for the quadratic assignment problem

11/05/2011
by   Gerald Paul, et al.
0

The quadratic assignment problem (QAP) is one of the most difficult combinatorial optimization problems. One of the most powerful and commonly used heuristics to obtain approximations to the optimal solution of the QAP is simulated annealing (SA). We present an efficient implementation of the SA heuristic which performs more than 100 times faster then existing implementations for large problem sizes and a large number of SA iterations.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/18/2014

A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem

Quadratic Assignment Problem (QAP) is an NP-hard combinatorial optimizat...
research
03/29/2019

An Upper Bound for Minimum True Matches in Graph Isomorphism with Simulated Annealing

Graph matching is one of the most important problems in graph theory and...
research
02/18/2020

Constrained Multiagent Rollout and Multidimensional Assignment with the Auction Algorithm

We consider an extension of the rollout algorithm that applies to constr...
research
01/06/2020

Clustering Binary Data by Application of Combinatorial Optimization Heuristics

We study clustering methods for binary data, first defining aggregation ...
research
10/19/2019

Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem

The Quadratic Assignment Problem (QAP) is a well-known permutation-based...
research
03/29/2019

How to Estimate the Ability of a Metaheuristic Algorithm to Guide Heuristics During Optimization

Metaheuristics are general methods that guide application of concrete he...
research
07/20/2023

GPU-accelerated Parallel Solutions to the Quadratic Assignment Problem

The Quadratic Assignment Problem (QAP) is an important combinatorial opt...

Please sign up or login with your details

Forgot password? Click here to reset