4 research outputs found
Técnicas de optimización paralelas : esquema hÃbrido basado en hiperheurÃsticas 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 metaheurÃsticas son una de las técnicas más usadas. El uso de metaheurÃsticas ha posibilitado la resolución de una gran cantidad de problemas en diferentes campos. Esto se debe a que las metaheurÃsticas 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 hiperheurÃsticas, adaptan los valores usados a las necesidades de cada fase de optimización. Además, estas técnicas permiten usar varias metaheurÃsticas 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 hiperheurÃsticas e integrarlas en el modelo paralelo basado en islas. Estas técnicas se han usado para controlar los parámetros de varias metaheurÃsticas evolutivas. Se han definido diversas hiperheurÃsticas 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 hiperheurÃsticas 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 hiperheurÃsticas 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
Novelty-driven cooperative coevolution
Cooperative coevolutionary algorithms (CCEAs) rely on multiple coevolving populations for the evolution of solutions composed of coadapted components. CCEAs enable, for instance, the evolution of cooperative multiagent systems composed of heterogeneous agents, where each agent is modelled as a component of the solution. Previous works have, however, shown that CCEAs are biased toward stability: the evolutionary process tends to converge prematurely to stable states instead of (near-)optimal solutions. In this study, we show how novelty search can be used to avoid the counterproductive attraction to stable states in coevolution. Novelty search is an evolutionary technique that drives evolution toward behavioural novelty and diversity rather than exclusively pursuing a static objective. We evaluate three novelty-based approaches that rely on, respectively (1) the novelty of the team as a whole, (2) the novelty of the agents’ individual behaviour, and (3) the combination of the two. We compare the proposed approaches with traditional fitness-driven cooperative coevolution in three simulated multirobot tasks. Our results show that team-level novelty scoring is the most effective approach, significantly outperforming fitness-driven coevolution at multiple levels. Novelty-driven cooperative coevolution can substantially increase the potential of CCEAs while maintaining a computational complexity that scales well with the number of populations.info:eu-repo/semantics/publishedVersio
A fuzzy logic controller applied to a diversity-based multi-objective evolutionary algorithm for single-objective optimisation
In recent years, Multi-Objective Evolutionary Algorithms (MOEAS) that consider diversity as an objective have been used to tackle single-objective optimisation prob- lems. The ability to deal with premature convergence has been greatly improved with these schemes. However, they usually increase the number of free parameters that need to be tuned. To improve results and avoid the tedious hand- tuning of algorithms, the use of automated parameter con- trol approaches that are able to adapt parameter values dur- ing the course of an evolutionary run are becoming more common in the field of Evolutionary Computation (EC). This research focuses on the application of parameter control approaches to diversity-based moeas. Two external parame- ter control methods are investigated; a novel method based on Fuzzy Logic and a recently proposed Hyper-heuristic. These are compared to an internal control method that uses self- adaptation. An extensive comparison of the three methods is carried out using a set of single-objective benchmark prob- lems of diverse complexity. Analyses include comparisons to a wide range of schemes with fixed parameters and to a single-objective approach. The results show that the fuzzy logic and hyper-heuristic methods are able to find similar or better solutions than the fixed parameter methods for a sig- nificant number of problems, with considerable savings in computational resources and time, whereas the self-adaptive strategy provides little benefit. Finally, we also demonstrate that the controlled diversity-based moea outperforms the single-objective scheme in most cases, thus showing the ben- efits of solving single-objective problems through diversity-based multi-objective schemes
Novel approaches to cooperative coevolution of heterogeneous multiagent systems
Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2017Heterogeneous multirobot systems are characterised by the morphological and/or behavioural heterogeneity of their constituent robots. These systems have a number of advantages over the more common homogeneous multirobot systems: they can leverage specialisation for increased efficiency, and they can solve tasks that are beyond the reach of any single type of robot, by combining the capabilities of different robots. Manually designing control for heterogeneous systems is a challenging endeavour, since the desired system behaviour has to be decomposed into behavioural rules for the individual robots, in such a way that the team as a whole cooperates and takes advantage of specialisation. Evolutionary robotics is a promising alternative that can be used to automate the synthesis of controllers for multirobot systems, but so far, research in the field has been mostly focused on homogeneous systems, such as swarm robotics systems. Cooperative coevolutionary algorithms (CCEAs) are a type of evolutionary algorithm that facilitate the evolution of control for heterogeneous systems, by working over a decomposition of the problem. In a typical CCEA application, each agent evolves in a separate population, with the evaluation of each agent depending on the cooperation with agents from the other coevolving populations. A CCEA is thus capable of projecting the large search space into multiple smaller, and more manageable, search spaces. Unfortunately, the use of cooperative coevolutionary algorithms is associated with a number of challenges. Previous works have shown that CCEAs are not necessarily attracted to the global optimum, but often converge to mediocre stable states; they can be inefficient when applied to large teams; and they have not yet been demonstrated in real robotic systems, nor in morphologically heterogeneous multirobot systems. In this thesis, we propose novel methods for overcoming the fundamental challenges in cooperative coevolutionary algorithms mentioned above, and study them in multirobot domains: we propose novelty-driven cooperative coevolution, in which premature convergence is avoided by encouraging behavioural novelty; and we propose Hyb-CCEA, an extension of CCEAs that places the team heterogeneity under evolutionary control, significantly improving its scalability with respect to the team size. These two approaches have in common that they take into account the exploration of the behaviour space by the evolutionary process. Besides relying on the fitness function for the evaluation of the candidate solutions, the evolutionary process analyses the behaviour of the evolving agents to improve the effectiveness of the evolutionary search. The ultimate goal of our research is to achieve general methods that can effectively synthesise controllers for heterogeneous multirobot systems, and therefore help to realise the full potential of this type of systems. To this end, we demonstrate the proposed approaches in a variety of multirobot domains used in previous works, and we study the application of CCEAs to new robotics domains, including a morphological heterogeneous system and a real robotic system.Fundação para a Ciência e a Tecnologia (FCT, PEst-OE/EEI/LA0008/2011