AUTOMATIC VERIFICATION OF OPTIMIZATION ALGORITHMS: A CASE STUDY OF A QUADRATIC ASSIGNMENT PROBLEM SOLVER

2011 ◽  
Vol 21 (02) ◽  
pp. 289-307 ◽  
Author(s):  
ROBERT MERKEL ◽  
DAOMING WANG ◽  
HUIMIN LIN ◽  
TSONG YUEH CHEN
Keyword(s):  
Assignment Problem ◽  
Optimization Problem ◽  
Quadratic Assignment Problem ◽  
Relative Effectiveness ◽  
Mathematical Optimization ◽  
Quadratic Assignment ◽  
Problem Solver ◽  
Metamorphic Testing ◽  
Single Relation

Metamorphic testing is a technique for the verification of software output without a complete testing oracle. Mathematical optimization, implemented in software, is a problem for which verification can often be challenging. In this paper, we apply metamorphic testing to one such optimization problem, the quadratic assignment problem (QAP). From simple observations of the properties of the QAP, we describe how to derive a number of metamorphic relations useful for verifying the correctness of a QAP solver. We then compare the effectiveness of these metamorphic relations, in "killing" mutant versions of an exact QAP solver, to a simulated oracle. We show that metamorphic testing can be as effective as the simulated oracle for killing mutants. We examine the relative effectiveness of different metamorphic relations, both singly and in combination, and conclude that combining metamorphic relations can be significantly more effective than using a single relation.

AN IMPROVED HYBRID OPTIMIZATION ALGORITHM FOR THE QUADRATIC ASSIGNMENT PROBLEM

2005 ◽  
Vol 9 (2) ◽  
pp. 149-168 ◽  
Author(s):  
A. Misevičius
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Optimization Algorithm ◽  
Assignment Problem ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Quadratic Assignment Problem ◽  
Hybrid Optimization ◽  
Quadratic Assignment ◽  
Hybrid Optimization Algorithm

In this paper, we present an improved hybrid optimization algorithm, which was applied to the hard combinatorial optimization problem, the quadratic assignment problem (QAP). This is an extended version of the earlier hybrid heuristic approach proposed by the author. The new algorithm is distinguished for the further exploitation of the idea of hybridization of the well‐known efficient heuristic algorithms, namely, simulated annealing (SA) and tabu search (TS). The important feature of our algorithm is the so‐called “cold restart mechanism”, which is used in order to avoid a possible “stagnation” of the search. This strategy resulted in very good solutions obtained during simulations with a number of the QAP instances (test data). These solutions show that the proposed algorithm outperforms both the “pure” SA/TS algorithms and the earlier author's combined SA and TS algorithm. Key words: hybrid optimization, simulated annealing, tabu search, quadratic assignment problem, simulation.

scholarly journals A comparison of nature inspired algorithms for the quadratic assignment problem

2017 ◽  
Vol 65 (4) ◽  
pp. 513-522 ◽  
Author(s):  
W. Chmiel ◽  
P. Kadłuczka ◽  
J. Kwiecień ◽  
B. Filipowicz
Keyword(s):  
Assignment Problem ◽  
Optimization Problem ◽  
Quadratic Assignment Problem ◽  
Search Method ◽  
Bees Algorithm ◽  
Quadratic Assignment ◽  
Ant Algorithm ◽  
Neighbourhood Size ◽  
Neighbourhood Search ◽  
Nature Inspired Algorithms

AbstractThis paper presents an application of the ant algorithm and bees algorithm in optimization of QAP problem as an example of NP-hard optimization problem. The experiments with two types of algorithms: the bees algorithm and the ant algorithm were performed for the test instances of the quadratic assignment problem from QAPLIB, designed by Burkard, Karisch and Rendl. On the basis of the experiments results, an influence of particular elements of algorithms, including neighbourhood size and neighbourhood search method, will be determined.

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