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

Optimising Real-World Traffic Cycle Programs by Using Evolutionary Computation

Traffic congestion, and the consequent loss of time, money, quality of life, and higher pollution, is currently one of the most important problems in cities, and several approaches have been proposed to reduce it. In this paper, we propose a novel formulation of the traffic light scheduling problem in order to alleviate it. This novel formulation of the problem allows more realistic scenarios to be modeled, and as a result, it becomes much harder to solve in comparison to previous formulations. The proposal of more advanced and efficient techniques than those applied in past research is thus required. We propose the application of diversity-based multi-objective optimizers, which have shown to provide promising results when addressing single-objective problems. The wide experimental evaluation performed over a set of real-world instances demonstrates the good performance of our proposed diversity-based multi-objective method to tackle traffic at a large scale, especially in comparison to the best-performing single-objective optimizer previously proposed in the literature. Consequently, in this paper, we provide new state-of-the-art algorithmic schemes to address the traffic light scheduling problem that can deal with a whole city, instead of just a few streets and junctions, with a higher level of detail than the one found in present studies due to our micro-analysis of streets

Parallel transfer evolution algorithm

Parallelization of an evolutionary algorithm takes the advantage of modular population division and information exchange among multiple processors. However, existing parallel evolutionary algorithms are rather ad hoc and lack a capability of adapting to diverse problems. To accommodate a wider range of problems and to reduce algorithm design costs, this paper develops a parallel transfer evolution algorithm. It is based on the island-model of parallel evolutionary algorithm and, for improving performance, transfers both the connections and the evolutionary operators from one sub-population pair to another adaptively. Needing no extra upper selection strategy, each sub-population is able to select autonomously evolutionary operators and local search operators as subroutines according to both the sub-population's own and the connected neighbor's ranking boards. The parallel transfer evolution is tested on two typical combinatorial optimization problems in comparison with six existing ad-hoc evolutionary algorithms, and is also applied to a real-world case study in comparison with five typical parallel evolutionary algorithms. The tests show that the proposed scheme and the resultant PEA offer high flexibility in dealing with a wider range of combinatorial optimization problems without algorithmic modification or redesign. Both the topological transfer and the algorithmic transfer are seen applicable not only to combinatorial optimization problems, but also to non-permutated complex problems