External memory in a hybrid ant colony system for a 2D strip packing

In this paper we present a study of an Ant Colony System (ACS) for the two-dimensional strip packing problem. In our computational study, we emphasize the influence of incorporating an external memory, which store partial packing patterns, regarding solution quality and execution times. The stored partial solutions are used by the ants in the construction of their solutions to provide further exploitation around potential solutions. We show that our external memory based ACS algorithm to the 2SPP was able to devise solutions of quality comparable to that of those reported by an existing ACS but exhibiting low execution times.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

Copyright @ Springer Science + Business Media. All rights reserved.The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimisation problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and Grant EP/E060722/02

External memory in a hybrid ant colony system for a 2D strip packing

In this paper we present a study of an Ant Colony System (ACS) for the two-dimensional strip packing problem. In our computational study, we emphasize the influence of incorporating an external memory, which store partial packing patterns, regarding solution quality and execution times. The stored partial solutions are used by the ants in the construction of their solutions to provide further exploitation around potential solutions. We show that our external memory based ACS algorithm to the 2SPP was able to devise solutions of quality comparable to that of those reported by an existing ACS but exhibiting low execution times.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Ant Colony Optimization with External Memory of Each Ant

Ant Colony Optimization (ACO)はDorigoに提案されて以降，様々なアルゴリズムの拡張が行われている。従来のACOでは個々のアリが独自の情報を持つことは無く，グローバルな情報のみに従って探索を行っていた。本稿では個々のアリの記憶情報を探索に利用するACOを提案する。さらに，個々のアリの記憶が一定確率で忘れられるケースも考える。また 性能比較実験にはTSPライブラリーの標準テスト問題を使い，拡張アルゴリズムの有効性を示す。Since Ant Colony Optimization (ACO) algorithm was introduced by Dorigo in 1992, several researchers have enhanced it. Each ant in basic ACO algorithm has no long-term memory; it searches using only pheromone information. In this paper, we propose a variant of ACO algorithm that uses external memory of each ant to seek an optimum solution. Moreover, it incorporates not only the case in which each ant’s memory is permanent but also the case in which the memory is lost with a certain probability. The effectiveness of our proposed algorithm is demonstrated by testing with benchmark test problems from the TSP library (TSPLIB)

Ant Colony Optimization para la resolución del Problema de Steiner Generalizado

Esta tesis presenta un estudio de la metaheurïstica Ant Colony Optimization (ACO) y de la aplicación de técnicas de computación de alto desempeño a dicha metaheurïstica. En particular, se aborda la aplicación de ACO a la resolución del Problema de Steiner Generalizado (GSP). El GSP consiste en el diseño de una subred de costo mínimo que verifique ciertos requerimientos prefijados de conexión entre pares de nodos distinguidos. En el trabajo se presentan versiones ACO con dos enfoques constructivos de la solución distintos. El primero de los enfoques se basa en incorporar aristas hasta completar un camino, mientras que el segundo determina los K caminos más cortos y realiza una selección entre ellos. También se propone una novedosa formulación de un modelo celular aplicado a la metaheurística ACO y su posible paralelización Se incluye los resultados de un estudio experimental exhaustivo de todas las propuestas formuladas en este trabajo, comprendiendo la evaluación de los enfoques basados en aristas y en caminos y el analizas del efecto del tamaño de la población, de la cantidad de caminos y de incorporar operadores de búsqueda local para el enfoque basado en caminos. El estudio permitió comprobar que la utilización de un enfoque basado en caminos con la incorporación del operador de búsqueda local iterado obtiene resultados competitivos con las mejores técnicas disponibles en la actualidad. Asimismo, se evaluaron las versiones secuencial y paralela del modelo celular propuesto, constatándose que el desempeño computacional de la implementación paralela es muy promisoria, aunque se producen leves pérdidas en la calidad de las soluciones con relación a estructurar la población en la forma tradiciona

Understanding how Knowledge is exploited in Ant Algorithms

Centre for Intelligent Systems and their ApplicationsAnt algorithms were first written about in 1991 and since then they have been applied to many problems with great success. During these years the algorithms themselves have been modified for improved performance and also been influenced by research in other fields. Since the earliest Ant algorithms, heuristics and local search have been the primary knowledge sources. This thesis asks the question "how is knowledge used in Ant algorithms?" To answer this question three Ant algorithms are implemented. The first is the Graph based Ant System (GBAS), a theoretical model not yet implemented, and the others are two influential algorithms, the Ant System and Max-Min Ant System. A comparison is undertaken to show that the theoretical model empirically models what happens in the other two algorithms. Therefore, this chapter explores whether different pheromone matrices (representing the internal knowledge) have a significant effect on the behaviour of the algorithm. It is shown that only under extreme parameter settings does the behaviour of Ant System and Max-Min Ant System differ from that of GBAS. The thesis continues by investigating how inaccurate knowledge is used when it is the heuristic that is at fault. This study reveals that Ant algorithms are not good at dealing with this information, and if they do use a heuristic they must rely on it relating valid guidance. An additional benefit of this study is that it shows heuristics may offer more control over the exploration-exploitation trade-off than is afforded by other parameters. The second point where knowledge enters the algorithm is through the local search. The thesis looks at what happens to the performance of the Ant algorithms when a local search is used and how this affects the parameters of the algorithm. It is shown that the addition of a local search method does change the behaviour of the algorithm and that the strength of the method has a strong influence on how the parameters are chosen. The final study focuses on whether Ant algorithms are effective for driving a local search method. The thesis demonstrates that these algorithms are not as effective as some simpler fixed and variable neighbourhood search methods