Tailoring hyper-heuristics to specific instances of a scheduling problem using affinity and competence functions

Hyper-heuristics are high level heuristics which coordinate lower level ones to solve a given problem. Low level heuristics, however, are not all as competent/good as each other at solving the given problem and some do not work together as well as others. Hence the idea of measuring how good they are (competence) at solving the problem and how well they work together (their affinity). Models of the affinity and competence properties are suggested and evaluated using previous information on the performance of the simple low level heuristics. The resulting model values are used to improve the performance of the hyper-heuristic by tailoring it not only to the specific problem but the specific instance being solved. The test case is a hard combinatorial problem, namely the Hybrid Flow Shop scheduling problem. Numerical results on randomly generated as well as real-world instances are included

An improved constraint satisfaction adaptive neural network for job-shop scheduling

Copyright @ Springer Science + Business Media, LLC 2009This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and in part by the National Nature Science Fundation of China under Grant 60821063 and National Basic Research Program of China under Grant 2009CB320601

On Constructing Ensembles for Combinatorial Optimisation

Although the use of ensemble methods in machine-learning is ubiquitous due to their proven ability to outperform their constituent algorithms, ensembles of optimisation algorithms have received relatively little attention. Existing approaches lag behind machine-learning in both theory and practice, with no principled design guidelines available. In this paper, we address fundamental questions regarding ensemble composition in opti-misation using the domain of bin-packing as a example; in particular we investigate the trade-off between accuracy and diversity, and whether diversity metrics can be used as a proxy for constructing an ensemble, proposing a number of novel metrics for comparing algorithm diversity. We find that randomly composed ensembles can outperform ensembles of high-performing algorithms under certain conditions and that judicious choice of diversity metric is required to construct good ensembles. The method and findings can be generalised to any meta-heuristic ensemble, and lead to better understanding of how to undertake principled ensemble design

A Linear Combination of Heuristics Approach to Spatial Sampling Hyperspectral Data for Target Tracking

Persistent surveillance of the battlespace results in better battlespace awareness which aids in obtaining air superiority, winning battles, and saving friendly lives. Although hyperspectral imagery (HSI) data has proven useful for discriminating targets, it presents many challenges as a useful tool in persistent surveillance. A new sensor under development has the potential of overcoming these challenges and transforming our persistent surveillance capability by providing HSI data for a limited number of pixels and grayscale video for the remainder. The challenge of exploiting this new sensor is determining where the HSI data in the sensor\u27s field of view will be the most useful. The approach taken is to use a utility function with components of equal dispersion, periodic poling, missed measurements, and predictive probability of association error (PPAE). The relative importance or optimal weighting of the different types of TOI is accomplished by a genetic algorithm using a multi-objective problem formulation. Experiments show using the utility function with equal weighting results in superior target tracking compared to any individual component by itself, and the equal weighting in close to the optimal solution. The new sensor is successfully exploited resulting in improved persistent surveillance

Hyper-heuristic decision tree induction

A hyper-heuristic is any algorithm that searches or operates in the space of heuristics as opposed to the space of solutions. Hyper-heuristics are increasingly used in function and combinatorial optimization. Rather than attempt to solve a problem using a fixed heuristic, a hyper-heuristic approach attempts to find a combination of heuristics that solve a problem (and in turn may be directly suitable for a class of problem instances). Hyper-heuristics have been little explored in data mining. This work presents novel hyper-heuristic approaches to data mining, by searching a space of attribute selection criteria for decision tree building algorithm. The search is conducted by a genetic algorithm. The result of the hyper-heuristic search in this case is a strategy for selecting attributes while building decision trees. Most hyper-heuristics work by trying to adapt the heuristic to the state of the problem being solved. Our hyper-heuristic is no different. It employs a strategy for adapting the heuristic used to build decision tree nodes according to some set of features of the training set it is working on. We introduce, explore and evaluate five different ways in which this problem state can be represented for a hyper-heuristic that operates within a decisiontree building algorithm. In each case, the hyper-heuristic is guided by a rule set that tries to map features of the data set to be split by the decision tree building algorithm to a heuristic to be used for splitting the same data set. We also explore and evaluate three different sets of low-level heuristics that could be employed by such a hyper-heuristic. This work also makes a distinction between specialist hyper-heuristics and generalist hyper-heuristics. The main difference between these two hyperheuristcs is the number of training sets used by the hyper-heuristic genetic algorithm. Specialist hyper-heuristics are created using a single data set from a particular domain for evolving the hyper-heurisic rule set. Such algorithms are expected to outperform standard algorithms on the kind of data set used by the hyper-heuristic genetic algorithm. Generalist hyper-heuristics are trained on multiple data sets from different domains and are expected to deliver a robust and competitive performance over these data sets when compared to standard algorithms. We evaluate both approaches for each kind of hyper-heuristic presented in this thesis. We use both real data sets as well as synthetic data sets. Our results suggest that none of the hyper-heuristics presented in this work are suited for specialization – in most cases, the hyper-heuristic’s performance on the data set it was specialized for was not significantly better than that of the best performing standard algorithm. On the other hand, the generalist hyper-heuristics delivered results that were very competitive to the best standard methods. In some cases we even achieved a significantly better overall performance than all of the standard methods

Híper-heurísticas com aprendizagem

A otimização nos sistemas de suporte à decisão atuais assume um carácter fortemente interdisciplinar relacionando-se com a necessidade de integração de diferentes técnicas e paradigmas na resolução de problemas reais complexos, sendo que a computação de soluções ótimas em muitos destes problemas é intratável. Os métodos de pesquisa heurística são conhecidos por permitir obter bons resultados num intervalo temporal aceitável. Muitas vezes, necessitam que a parametrização seja ajustada de forma a permitir obter bons resultados. Neste sentido, as estratégias de aprendizagem podem incrementar o desempenho de um sistema, dotando-o com a capacidade de aprendizagem, por exemplo, qual a técnica de otimização mais adequada para a resolução de uma classe particular de problemas, ou qual a parametrização mais adequada de um dado algoritmo num determinado cenário. Alguns dos métodos de otimização mais usados para a resolução de problemas do mundo real resultaram da adaptação de ideias de várias áreas de investigação, principalmente com inspiração na natureza - Meta-heurísticas. O processo de seleção de uma Meta-heurística para a resolução de um dado problema é em si um problema de otimização. As Híper-heurísticas surgem neste contexto como metodologias eficientes para selecionar ou gerar heurísticas (ou Meta-heurísticas) na resolução de problemas de otimização NP-difícil. Nesta dissertação pretende-se dar uma contribuição para o problema de seleção de Metaheurísticas respetiva parametrização. Neste sentido é descrita a especificação de uma Híperheurística para a seleção de técnicas baseadas na natureza, na resolução do problema de escalonamento de tarefas em sistemas de fabrico, com base em experiência anterior. O módulo de Híper-heurística desenvolvido utiliza um algoritmo de aprendizagem por reforço (QLearning), que permite dotar o sistema da capacidade de seleção automática da Metaheurística a usar no processo de otimização, assim como a respetiva parametrização. Finalmente, procede-se à realização de testes computacionais para avaliar a influência da Híper- Heurística no desempenho do sistema de escalonamento AutoDynAgents. Como conclusão genérica, é possível afirmar que, dos resultados obtidos é possível concluir existir vantagem significativa no desempenho do sistema quando introduzida a Híper-heurística baseada em QLearning.Optimization in current decision support systems has a highly interdisciplinary nature related with the need to integrate different techniques and paradigms in solving real-world complex problems. Computing optimal solutions in many of these problems are unmanageable. Heuristic search methods are known to obtain good results in an acceptable time interval. Often, parameters need to be adjusted to allow good results. In this sense, learning strategies can enhance the performance of a system, providing it with the ability to learn, for instance, the most suitable optimization technique for solving a particular class of problems, or the most suitable parameterization of a given algorithm on a given scenario. Some of the most used optimization methods for solving real world problems resulted from the adaptation of ideas from several areas of research, especially the ones inspired by nature - Metaheuristics. The process of selecting a Metaheuristics for solving a given problem is by itself an optimization problem. The Hyper-heuristics arise in this context as efficient methodologies for selecting or generating heuristics (or Metaheuristics) to solve NP-hard optimization problems. This thesis aims to make a contribution to the problem of selection of Metaheuristics and respective parameterization. In this sense, the specification of a Hyper-heuristic is describes for the selection of techniques based in nature, in solving the problem of scheduling in manufacturing systems, based on previous experience. The developed Hyper-heuristic module uses a reinforcement learning algorithm (Q-Learning), which enables the system with the ability to autonomously select the Metaheuristics to use in optimization process as well as the respective parameters. Finally, a computational study was carried out to evaluate the influence of the Hyper-heuristics on the performance of the AutoDynAgents system. As a general conclusion, we can say from the results obtained that there is significant advantage in using the system with the Q-Learning based Hyper-heuristic

Phenotype Operators for Improved Performance of Heuristic Encoding Within Genetic Algorithms

Many approaches to applying Genetic Algorithms (GAs) to Nondeterministic Polynomial time Complete (NPC) problems involve population members encoded directly from the problem solution space. While this technique enables trivial mapping of the population members to solutions, it can cause complex problems for GA operators as they attempt to direct the evolution of the population toward more promising areas of the solution space. These operators, using inspiration from genetics and evolution in the biological world, combine and manipulate the current population to produce a new population that, it is hoped, will eventually converge toward better solutions to the original problem. However, many problems, especially graph-space problems, cannot be so easily manipulated when GA members consist of direct encodings. In such cases, GA operators must perform awkward transformations to convert the progeny into viable solutions. Here is where heuristic encoding comes into play, in that any combination of genes will produce a viable solution. However, this additional level of abstraction does cause other problems and tends to weaken the guiding effects of traditional GA operators. Thus, I have designed custom GA operators that mitigate these problems by using the solutions produced by the heuristic encoded members to better guide the manipulation when producing the next generation. This dissertation shows that heuristic encoding is an effective technique for the representation of solutions to graph-space problems. It also shows that, when using heuristic encoding, GAs with traditional operators perform well compared to more direct encoding techniques. Finally it shows the combination of heuristic encoding and GA operators designed to work with them increases GA performance and can be competitive with other techniques. I believe that these techniques will also work well for other types of problems for which GAs are commonly applied

An investigation of the ant-based hyper-heuristic for capacitated vehicle routing problem and traveling salesman problem

A brief observation on recent research of routing problems shows that most of the methods used to tackle the problems are using heuristics and metaheuristics; and they often use problem specific knowledge to build or improve solutions. In the last few years, research on hyper-heuristic has been investigated which aims to raise the generality of optimisation systems. This thesis is concerned with the investigation of ant-based hyper-heuristic. Ant algorithms have been applied to vehicle routing problems and have produced competitive results. Therefore, it is assumed that there is a reasonable possibility that ant-based hyperheuristic could perform well for the problem. The thesis first surveys the literature for some common solution methodologies for optimisation problems and explores in some detail the ant algorithms and ant algorithm hyperheuristic methods. Furthermore, the literature specifically concerns with routing problems; the capacitated routing problem (CVRP) and the travelling salesman problem (TSP). The thesis studies the ant system algorithm and further proposes the ant algorithm hyper-heuristic, which introduces a new pheromone update rule in order to improve its performance. The proposed approach, called the ant-based hyper-heuristic is tested to two routing problems; the CVRP and TSP. Although it does not produce any best known results, the experimental results have shown that it is competitive with other methods. Most importantly, it demonstrates how simple and easy to implement low level heuristics, with no extensive parameter tuning. Further analysis shows that the approach possesses learning mechanism when compared to random hyper-heuristic. The approach investigates the number of low level heuristics appropriate and found out that the more low level heuristics used, the better solution is generated. In addition an ACO hyper-heuristic which has two categories of pheromone updates is developed. However, ant-based hyper-heuristic performs better and this is inconsistent with the performance of ACO algorithm in the literature. In TSP, we utilise two different categories of low level heuristics, the TSP heuristics and the CVRP heuristics that were previously used for the CVRP. From the observation, it can be seen that by using any heuristics for the same class of problems, ant-based hyper-heuristic is seen to be able to produce competitive results. This has demonstrated that the ant-based hyper-heuristic is a reusable method. One major advantage of this work is the usage of the same parameter for all problem instances with simple moves and swap procedures. It is hoped that in the future, results obtained will be better than current results by using better intelligent low level heuristics

Sélection et réglage de paramètres pour l'optimisation de logiciels d'ordonnancement industriel

L’utilisation d’un logiciel d’ordonnancement industriel fait intervenir une multitude de paramètres dont le réglage influence fortement la qualité des résultats. A l’heure actuelle, ce réglage est effectué de façon manuelle, après un travail souvent fastidieux au cours de l’installation initiale du logiciel’ De plus, une fois spécifiées, les valeurs de ces paramètres sont rarement remises en cause par les utilisateurs, du fait de leur manque d’expérience et du nombre important de paramètres à ajuster. L’idée que nous développons ici consiste à utiliser des métaheuristiques pour automatiser cette tâche. Deux problèmes seront abordés : la sélection des paramètres pertinents et leur réglage en fonction des exigences de l’utilisateur. Nous proposons de résoudre ces deux problèmes de façon simultanée, en introduisant des stratégies de sélection au sein des métaheuristiques. Cette approche est appliquée au logiciel d’ordonnancement Ortems® et validée sur plusieurs cas industriels. ABSTRACT : The use of scheduling software requires to set-up a number of parameters that have a direct influence on the schedule quality. Nowadays, this set-up is obtained manually after an extensive effort during initial software installation. Moreover, this set-up is rarely called into question by users, due to their lack of experience and to the high number of parameters involved. It is suggested in this thesis the use of metaheuristics to automate this task. Two problems are considered: selection of relevant parameters and their tuning according to user requirements. We suggest here an approach to solve these problems simultaneously, based on the combination of metaheuristics with some parameter selection strategies. An implementation framework has been developed and tested on an industrial scheduler, named Ortems®. The first results of the use of this framework on real industrial databases are described and commented

An investigation of the ant-based hyper-heuristic for capacitated vehicle routing problem and traveling salesman problem

A brief observation on recent research of routing problems shows that most of the methods used to tackle the problems are using heuristics and metaheuristics; and they often use problem specific knowledge to build or improve solutions. In the last few years, research on hyper-heuristic has been investigated which aims to raise the generality of optimisation systems. This thesis is concerned with the investigation of ant-based hyper-heuristic. Ant algorithms have been applied to vehicle routing problems and have produced competitive results. Therefore, it is assumed that there is a reasonable possibility that ant-based hyperheuristic could perform well for the problem. The thesis first surveys the literature for some common solution methodologies for optimisation problems and explores in some detail the ant algorithms and ant algorithm hyperheuristic methods. Furthermore, the literature specifically concerns with routing problems; the capacitated routing problem (CVRP) and the travelling salesman problem (TSP). The thesis studies the ant system algorithm and further proposes the ant algorithm hyper-heuristic, which introduces a new pheromone update rule in order to improve its performance. The proposed approach, called the ant-based hyper-heuristic is tested to two routing problems; the CVRP and TSP. Although it does not produce any best known results, the experimental results have shown that it is competitive with other methods. Most importantly, it demonstrates how simple and easy to implement low level heuristics, with no extensive parameter tuning. Further analysis shows that the approach possesses learning mechanism when compared to random hyper-heuristic. The approach investigates the number of low level heuristics appropriate and found out that the more low level heuristics used, the better solution is generated. In addition an ACO hyper-heuristic which has two categories of pheromone updates is developed. However, ant-based hyper-heuristic performs better and this is inconsistent with the performance of ACO algorithm in the literature. In TSP, we utilise two different categories of low level heuristics, the TSP heuristics and the CVRP heuristics that were previously used for the CVRP. From the observation, it can be seen that by using any heuristics for the same class of problems, ant-based hyper-heuristic is seen to be able to produce competitive results. This has demonstrated that the ant-based hyper-heuristic is a reusable method. One major advantage of this work is the usage of the same parameter for all problem instances with simple moves and swap procedures. It is hoped that in the future, results obtained will be better than current results by using better intelligent low level heuristics