Técnicas de optimización paralelas : esquema híbrido basado en hiperheurísticas y computación evolutiva

Optimisation is the process of selecting the best element fr om a set of available alternatives. Solutions are termed good or bad depending on its performance for a set of objectives. Several algorithms to deal with such kind of problems have been defined in the literature. Metaheuristics are one of the most prominent techniques. They are a class of modern heuristics whose main goal is to com bine heuristics in a problem independent way with the aim of improving their per formance. Meta- heuristics have reported high-quality solutions in severa l fields. One of the reasons of the good behaviour of metaheuristics is that they are defin ed in general terms. Therefore, metaheuristic algorithms can be adapted to fit th e needs of most real-life optimisation. However, such an adaptation is a hard task, and it requires a high computational and user effort. There are two main ways of reducing the effort associated to th e usage of meta- heuristics. First, the application of hyperheuristics and parameter setting strategies facilitates the process of tackling novel optimisation pro blems and instances. A hyperheuristic can be viewed as a heuristic that iterativel y chooses between a set of given low-level metaheuristics in order to solve an optim isation problem. By using hyperheuristics, metaheuristic practitioners do no t need to manually test a large number of metaheuristics and parameterisations for d iscovering the proper algorithms to use. Instead, they can define the set of configur ations which must be tested, and the model tries to automatically detect the be st-behaved ones, in order to grant more resources to them. Second, the usage of pa rallel environments might speedup the process of automatic testing, so high qual ity solutions might be achieved in less time. This research focuses on the design of novel hyperheuristic s and defines a set of models to allow their usage in parallel environments. Differ ent hyperheuristics for controlling mono-objective and multi-objective multi-po int optimisation strategies have been defined. Moreover, a set of novel multiobjectivisa tion techniques has been proposed. In addition, with the aim of facilitating the usage of multiobjectivi- sation, the performance of models that combine the usage of m ultiobjectivisation and hyperheuristics has been studied. The proper performance of the proposed techniques has been v alidated with a set of well-known benchmark optimisation problems. In addi tion, several practical and complex optimisation problems have been addressed. Som e of the analysed problems arise in the communication field. In addition, a pac king problem proposed in a competition has been faced up. The proposals for such pro blems have not been limited to use the problem-independent schemes. Inste ad, new metaheuristics, operators and local search strategies have been defined. Suc h schemes have been integrated with the designed parallel hyperheuristics wit h the aim of accelerating the achievement of high quality solutions, and with the aim of fa cilitating their usage. In several complex optimisation problems, the current best -known solutions have been found with the methods defined in this dissertation.Los problemas de optimización son aquellos en los que hay que elegir cuál es la solución más adecuada entre un conjunto de alternativas. Actualmente existe una gran cantidad de algoritmos que permiten abordar este tipo de problemas. Entre ellos, las metaheurísticas son una de las técnicas más usadas. El uso de metaheurísticas ha posibilitado la resolución de una gran cantidad de problemas en diferentes campos. Esto se debe a que las metaheurísticas son técnicas generales, con lo que disponen de una gran cantidad de elementos o parámetros que pueden ser adaptados a la hora de afrontar diferentes problemas de optimización. Sin embargo, la elección de dichos parámetros no es sencilla, por lo que generalmente se requiere un gran esfuerzo computacional, y un gran esfuerzo por parte del usuario de estas técnicas. Existen diversas técnicas que atenúan este inconveniente. Por un lado, existen varios mecanismos que permiten seleccionar los valores de dichos parámetros de forma automática. Las técnicas más simples utilizan valores fijos durante toda la ejecución, mientras que las técnicas más avanzadas, como las hiperheurísticas, adaptan los valores usados a las necesidades de cada fase de optimización. Además, estas técnicas permiten usar varias metaheurísticas de forma simultánea. Por otro lado, el uso de técnicas paralelas permite acelerar el proceso de testeo automático, reduciendo el tiempo necesario para obtener soluciones de alta calidad. El objetivo principal de esta tesis ha sido diseñar nuevas hiperheurísticas e integrarlas en el modelo paralelo basado en islas. Estas técnicas se han usado para controlar los parámetros de varias metaheurísticas evolutivas. Se han definido diversas hiperheurísticas que han permitido abordar tanto problemas mono-objetivo como problemas multi-objetivo. Además, se han definido un conjunto de multiobjetivizaciones, que a su vez se han beneficiado de las hiperheurísticas propuestas. Las técnicas diseñadas se han validado con algunos de los problemas de test más ampliamente utilizados. Además, se han abordado un conjunto de problemas de optimización prácticos. Concretamente, se han tratado tres problemas que surgen en el ámbito de las telecomunicaciones, y un problema de empaquetado. En dichos problemas, además de usar las hiperheurísticas y multiobjetivizaciones, se han definido nuevos algoritmos, operadores, y estrategias de búsqueda local. En varios de los problemas, el uso combinado de todas estas técnicas ha posibilitado obtener las mejores soluciones encontradas hasta el momento

HyperSPAM: A study on hyper-heuristic coordination strategies in the continuous domain

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This article proposes a simplistic algorithmic framework, namely hyperSPAM, composed of three search algorithms for addressing continuous optimisation problems. The Covariance Matrix Adaptation Evolution Strategy (CMAES) is activated at the beginning of the optimisation process as a preprocessing component for a limited budget. Subsequently, the produced solution is fed to the other two single-solution search algorithms. The first performs moves along the axes while the second makes use of a matrix orthogonalization to perform diagonal moves. Four coordination strategies, in the fashion of hyperheuristics, have been used to coordinate the two single-solution algorithms. One of them is a simple randomized criterion while the other three are based on a success based reward mechanism. The four implementations of the hyperSPAM framework have been tested and compared against each other and modern metaheuristics on an extensive set of problems including theoretical functions and real-world engineering problems. Numerical results show that the different versions of the framework display broadly a similar performance. One of the reward schemes appears to be marginally better than the others. The simplistic random coordination also displays a very good performance. All the implementations of hyperSPAM significantly outperform the other algorithms used for comparison

Using a Parallel Ensemble of Sequence-Based Selection Hyper-Heuristics for Electric Bus Scheduling

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordA Sequence-based Selection Hyper-Heuristic (SSHH) utilises a hidden Markov model (HMM) to generate sequences of low-level heuristics to apply to a given problem. The HMM represents learnt probabilistic relationships in transitioning from one heuristic to the next for generating good sequences. However, a single HMM will only represent one learnt behaviour pattern which may not be ideal. Furthermore, using a single HMM to generate sequences is sequential in manner but most processors are parallel in nature. Consequently, this paper proposes that the effectiveness and speed of SSHH can be improved by using multiple SSHH, an ensemble. These will be able to operate in parallel exploiting multi-core processor resources facilitating faster optimisation. Two methods of parallel ensemble SSHH are investigated, sharing the best found solution amongst SSHH instantiations or combining HMM information between SSHH models. The effectiveness of the methods are assessed using a real-world electric bus scheduling optimisation problem. Sharing best found solutions between ensembles of SSHH models that have differing sequence behaviours significantly improved upon sequential SSHH results with much lower run-times.Innovate UKCity Scienc

A multi-objective and evolutionary hyper-heuristic applied to the Integration and Test Order Problem

The field of Search-Based Software Engineering (SBSE) has widely utilized Multi-Objective Evolutionary Algorithms (MOEAs) to solve complex software engineering problems. However, the use of such algorithms can be a hard task for the software engineer, mainly due to the significant range of parameter and algorithm choices. To help in this task, the use of Hyper-heuristics is recommended. Hyper-heuristics can select or generate low-level heuristics while optimization algorithms are executed, and thus can be generically applied. Despite their benefits, we find only a few works using hyper-heuristics in the SBSE field. Considering this fact, we describe HITO, a Hyper-heuristic for the Integration and Test Order Problem, to adaptively select search operators while MOEAs are executed using one of the selection methods: Choice Function and Multi-Armed Bandit. The experimental results show that HITO can outperform the traditional MOEAs NSGA-II and MOEA/DD. HITO is also a generic algorithm, since the user does not need to select crossover and mutation operators, nor adjust their parameters