22 research outputs found
A public transport bus assignment problem: parallel metaheuristics assessment
Combinatorial Optimization Problems occur in a wide variety of contexts and generally
are NP-hard problems. At a corporate level solving this problems is of great importance
since they contribute to the optimization of operational costs. In this thesis we propose to solve the Public Transport Bus Assignment problem considering an heterogeneous fleet and line exchanges, a variant of the Multi-Depot Vehicle Scheduling Problem in which additional constraints are enforced to model a real life scenario.
The number of constraints involved and the large number of variables makes impracticable solving to optimality using complete search techniques. Therefore, we explore metaheuristics, that sacrifice optimality to produce solutions in feasible time. More concretely,
we focus on the development of algorithms based on a sophisticated metaheuristic,
Ant-Colony Optimization (ACO), which is based on a stochastic learning mechanism.
For complex problems with a considerable number of constraints, sophisticated metaheuristics may fail to produce quality solutions in a reasonable amount of time. Thus, we developed parallel shared-memory (SM) synchronous ACO algorithms, however, synchronism originates the straggler problem. Therefore, we proposed three SM asynchronous algorithms that break the original algorithm semantics and differ on the degree of concurrency allowed while manipulating the learned information.
Our results show that our sequential ACO algorithms produced better solutions than
a Restarts metaheuristic, the ACO algorithms were able to learn and better solutions were achieved by increasing the amount of cooperation (number of search agents). Regarding parallel algorithms, our asynchronous ACO algorithms outperformed synchronous ones in terms of speedup and solution quality, achieving speedups of 17.6x. The cooperation scheme imposed by asynchronism also achieved a better learning rate than the original one
Understanding how Knowledge is exploited in Ant Algorithms
Centre for Intelligent Systems and their ApplicationsAnt algorithms were first written about in 1991 and since then they have been applied
to many problems with great success. During these years the algorithms themselves
have been modified for improved performance and also been influenced by research in
other fields. Since the earliest Ant algorithms, heuristics and local search have been
the primary knowledge sources. This thesis asks the question "how is knowledge used
in Ant algorithms?"
To answer this question three Ant algorithms are implemented. The first is the Graph based
Ant System (GBAS), a theoretical model not yet implemented, and the others
are two influential algorithms, the Ant System and Max-Min Ant System. A comparison
is undertaken to show that the theoretical model empirically models what happens
in the other two algorithms. Therefore, this chapter explores whether different
pheromone matrices (representing the internal knowledge) have a significant effect on
the behaviour of the algorithm. It is shown that only under extreme parameter settings
does the behaviour of Ant System and Max-Min Ant System differ from that of GBAS.
The thesis continues by investigating how inaccurate knowledge is used when it is the
heuristic that is at fault. This study reveals that Ant algorithms are not good at dealing
with this information, and if they do use a heuristic they must rely on it relating valid
guidance. An additional benefit of this study is that it shows heuristics may offer more
control over the exploration-exploitation trade-off than is afforded by other parameters.
The second point where knowledge enters the algorithm is through the local search.
The thesis looks at what happens to the performance of the Ant algorithms when a
local search is used and how this affects the parameters of the algorithm. It is shown
that the addition of a local search method does change the behaviour of the algorithm
and that the strength of the method has a strong influence on how the parameters are
chosen.
The final study focuses on whether Ant algorithms are effective for driving a local
search method. The thesis demonstrates that these algorithms are not as effective as
some simpler fixed and variable neighbourhood search methods
Hybrid meta-heuristics for combinatorial optimization
Combinatorial optimization problems arise, in many forms, in vari- ous aspects of everyday life. Nowadays, a lot of services are driven by optimization algorithms, enabling us to make the best use of the available resources while guaranteeing a level of service. Ex- amples of such services are public transportation, goods delivery, university time-tabling, and patient scheduling.
Thanks also to the open data movement, a lot of usage data about public and private services is accessible today, sometimes in aggregate form, to everyone. Examples of such data are traffic information (Google), bike sharing systems usage (CitiBike NYC), location services, etc. The availability of all this body of data allows us to better understand how people interacts with these services. However, in order for this information to be useful, it is necessary to develop tools to extract knowledge from it and to drive better decisions. In this context, optimization is a powerful tool, which can be used to improve the way the available resources are used, avoid squandering, and improve the sustainability of services.
The fields of meta-heuristics, artificial intelligence, and oper- ations research, have been tackling many of these problems for years, without much interaction. However, in the last few years, such communities have started looking at each other’s advance- ments, in order to develop optimization techniques that are faster, more robust, and easier to maintain. This effort gave birth to the fertile field of hybrid meta-heuristics.openDottorato di ricerca in Ingegneria industriale e dell'informazioneopenUrli, Tommas
Evolutionary Computation
This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field