The Ant System Applied To The Quadratic Assignment

IEEE Transactions on Knowledge and Data Engineering, 1999

In recent years there has been growing interest in algorithms inspired by the observation of natural phenomena to define computational procedures which can solve complex problems.

Journal of the operational …, 1999

This paper presents HAS-QAP, a hybrid ant colony system coupled with a local search, applied to the quadratic assignment problem. HAS-QAP uses pheromone trail information to perform modifications on QAP solutions, unlike more traditional ant systems that use pheromone trail information to construct complete solutions. HAS-QAP is analysed and compared with some of the best heuristics available for the QAP: two versions of tabu search, namely, robust and reactive tabu search, hybrid genetic algorithm, and a simulated annealing method. Experimental results show that HAS-QAP and the hybrid genetic algorithm perform best on real world, irregular and structured problems due to their ability to find the structure of good solutions, while HAS-QAP performance is less competitive on random, regular and unstructured problems.

Applied Mathematics and Computation, 2006

Ant algorithm is a multi-agent systems inspired by the behaviors of real ant colonies function to solve optimization problems. In this paper an ant colony optimization algorithm is developed to solve the quadratic assignment problem. The local search process of the algorithm is simulated annealing. In the exploration of the search space, the evaluation of pheromones which are laid on the ground by ants is used. In this work, the algorithm is analyzed by using current problems in the literature and is compared with other metaheuristics.

Future Generation Computer Systems, 2001

Ant Colonies optimization take inspiration from the behavior of real ant colonies to solve optimization problems. This paper presents a parallel model for ant colonies to solve the quadratic assignment problem (QAP). The cooperation between simulated ants is provided by a pheromone matrix that plays the role of a global memory. The exploration of the search space is guided by the evolution of pheromones levels, while exploitation has been boosted by a tabu local search heuristic. Special care has also been taken in the design of a diversification phase, based on a frequency matrix. We give results that have been obtained on benchmarks from the QAP library. We show that they compare favorably with other algorithms dedicated for the QAP.

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008

The existing Ant Colony Optimization (ACO) Algorithms for the Quadratic Assignment Problem (QAP) are often combined with two kinds of Stochastic Local Search (SLS) methods: the 2-opt local search and the tabu local search. In this paper, these two SLS methods are respectively improved according to the properties of ACO and QAP. For the 2-opt local search, a new random walk strategy is used to avoid a quick stagnation into local optima. Moreover, a forwardlooking strategy is proposed to explore the neighborhood more thoroughly. In the case of tabu local search, a random walk strategy is also employed to avoid getting stuck at local optima. Experimental evaluation of the ACO algorithms combined with the improved local search proposed in this paper are conducted on problems from the well known QAPLIB library. The results demonstrate that each ACO algorithm, combined with its respective improved local search, has a better performance in terms of the quality of the solution returned than the ACO algorithm with the original local search techniques. Moreover, we also noticed that the improved methods outperform each other for different classes of problems.

The existing Ant Colony Optimization (ACO) Algorithms for the Quadratic Assignment Problem (QAP) are often combined with two kinds of Stochastic Local Search (SLS) methods: the 2-opt local search and the tabu local search. In this paper, these two SLS methods are respectively improved according to the properties of ACO and QAP. For the 2-opt local search, a new random walk strategy is used to avoid a quick stagnation into local optima. Moreover, a forward-looking strategy is proposed to explore the neighborhood more thoroughly. In the case of tabu local search, a random walk strategy is also employed to avoid getting stuck at local optima. Experimental evaluation of the ACO algorithms combined with the improved local search proposed in this paper are conducted on problems from the well known QAPLIB library. The results demonstrate that each ACO algorithm, combined with its respective improved local search, has a better performance, in terms of the quality of the solution returned, than the ACO algorithm with the original local search techniques. Moreover, we also noticed that the improved methods outperform each other for different classes of problems.

European Journal of Operational Research, 1995

This work compares the effectiveness of eight evolutionary heuristic algorithms applied to the Quadratic Assignment Problem (QAP), reputedly one of the most difficult combinatorial optimization problems. QAP is merely used as a carrier for the comparison: we do not attempt to compare any heuristics with solving algorithms specific for it. Results are given, both with respect to the best result achieved by each algorithm in a limited time span and to its speed of convergence to that result.

Bulletin of the Polish Academy of Sciences Technical Sciences, 2017

This 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.

In this paper, some further experiments with the genetic algorithm (GA) for the quadratic assignment problem (QAP) are described. We propose to use a particle-swarm-optimization-based approach for tuning the values of the parameters of the genetic algorithm for solving the QAP. The resulting combined self-adaptive swarm optimi-zation-genetic algorithm enables to efficiently auto-configure the control parameters for GA — which leads to excellent quality solutions, especially for the real-life like (structured) QAP instances.

2006

Abstract In recent decades, many meta-heuristics, including genetic algorithm (GA), ant colony optimization (ACO) and various local search (LS) procedures have been developed for solving a variety of NP-hard combinatorial optimization problems. Depending on the complexity of the optimization problem, a meta-heuristic method that may have proven to be successful in the past might not work as well.