29 research outputs found
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
A multi-objective hyper-heuristic based on choice function
Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems. We present a learning selection choice function based hyper-heuristic to solve multi-objective optimization problems. This high level approach controls and combines the strengths of three well-known multi-objective evolutionary algorithms (i.e. NSGAII, SPEA2 and MOGA), utilizing them as the low level heuristics. The performance of the proposed learning hyper-heuristic is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, the proposed hyper-heuristic is applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the hyper-heuristic approach when compared to the performance of each low level heuristic run on its own, as well as being compared to other approaches including an adaptive multi-method search, namely AMALGAM
Multi‐Objective Hyper‐Heuristics
Multi‐objective hyper‐heuristics is a search method or learning mechanism that operates over a fixed set of low‐level heuristics to solve multi‐objective optimization problems by controlling and combining the strengths of those heuristics. Although numerous papers on hyper‐heuristics have been published and several studies are still underway, most research has focused on single‐objective optimization. Work on hyper‐heuristics for multi‐objective optimization remains limited. This chapter draws attention to this area of research to help researchers and PhD students understand and reuse these methods. It also provides the basic concepts of multi‐objective optimization and hyper‐heuristics to facilitate a better understanding of the related research areas, in addition to exploring hyper‐heuristic methodologies that address multi‐objective optimization. Some design issues related to the development of hyper‐heuristic framework for multi‐objective optimization are discussed. The chapter concludes with a case study of multi‐objective selection hyper‐heuristics and its application on a real‐world problem
Choice function based hyper-heuristics for multi-objective optimization
A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems, while work on multi-objective selection hyper-heuristics is limited. This work presents one of the initial studies on selection hyper-heuristics combining a choice function heuristic selection methodology with great deluge and late acceptance as non-deterministic move acceptance methods for multi-objective optimization. A well-known hypervolume metric is integrated into the move acceptance methods to enable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the non-deterministic move acceptance, particularly great deluge when used as a component of a choice function based selection hyper-heuristic
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Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions
Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software Vol. 62 (2014), DOI: 10.1016/j.envsoft.2014.09.013The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts
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
Metaheuristic Optimization Frameworks: a Survey and Benchmarking
This paper performs an unprecedented comparative study of Metaheuristic optimization frameworks. As criteria for comparison a set of 271 features grouped in 30 characteristics and 6 areas has been selected. These features include the different metaheuristic techniques covered, mechanisms for solution encoding, constraint handling, neighborhood specification, hybridization, parallel and distributed computation, software engineering best practices, documentation and user interface, etc. A metric has been defined for each feature so that the scores obtained by a framework are averaged within each group of features, leading to a final average score for each framework. Out of 33 frameworks ten have been selected from the literature using well-defined filtering criteria, and the results of the comparison are analyzed with the aim of identifying improvement areas and gaps in specific frameworks and the whole set. Generally speaking, a significant lack of support has been found for hyper-heuristics, and parallel and distributed computing capabilities. It is also desirable to have a wider implementation of some Software Engineering best practices. Finally, a wider support for some metaheuristics and hybridization capabilities is needed