P-Complete Approximation Problems
Journal of the ACM (JACM)
Record breaking optimization results using the ruin and recreate principle
Journal of Computational Physics
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Tabu Search
On the Hardness of the Quadratic Assignment Problem with Metaheuristics
Journal of Heuristics
QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
A New Genetic Algorithm for the Quadratic Assignment Problem
INFORMS Journal on Computing
A tabu search algorithm for the quadratic assignment problem
Computational Optimization and Applications
The enhanced evolutionary tabu search and its application to the quadratic assignment problem
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Hybrid Metaheuristic for the Quadratic Assignment Problem
Computational Optimization and Applications
A GA-ACO-local search hybrid algorithm for solving quadratic assignment problem
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Computers and Operations Research
Very Large-Scale Neighborhood Search for the Quadratic Assignment Problem
INFORMS Journal on Computing
Paper: Robust taboo search for the quadratic assignment problem
Parallel Computing
Iterated robust tabu search for MAX-SAT
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
Solving the quadratic assignment problem with clues from nature
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
In this paper, we describe an implementation of the iterated tabu search (ITS) algorithm for the quadratic assignment problem (QAP), which is one of the well-known problems in combinatorial optimization. The medium- and large-scale QAPs are not, to this date, practically solvable to optimality, therefore heuristic algorithms are widely used. In the proposed ITS approach, intensification and diversification mechanisms are combined in a proper way. The goal of intensification is to search for good solutions in the neighbourhood of a given solution, while diversification is responsible for escaping from local optima and moving towards new regions of the search space. In particular, the following enhancements were implemented: new formula for fast evaluation of the objective function and efficient data structure; extended intensification mechanisms (including randomized tabu criterion, combination of tabu search and local search, dynamic tabu list maintaining); enhanced diversification strategy using periodic tabu tenure and special mutation procedure. The ITS algorithm is tested on the different instances taken from the QAP library QAPLIB. The results from the experiments demonstrate promising efficiency of the proposed algorithm, especially for the random QAP instances.