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http://hdl.handle.net/10609/43881
Title: Applications of Biased Randomization and Simheuristic Algorithms to Arc Routing and Facility Location Problems
Author: González Martín, Sergio
Director: Juan Pérez, Ángel Alejandro
Riera Terrén, Daniel  
Others: Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
Keywords: Biased Randomized Heuristics
Optimization
Real applications
Metaheuristics
Issue Date: 13-Mar-2015
Publisher: Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
Abstract: Most metaheuristics contain a randomness component, which is usually based on uniform randomization ¿i.e., the use of the Uniform probability distribution to make random choices. However, the Multi-start biased Randomization of classical Heuristics with Adaptive local search framework proposes the use of biased (non-uniform) randomization for the design of alternative metaheuristics -i.e., the use of skewed probability distributions such as the Geometric or Triangular ones. In some scenarios, this non-biased randomization has shown to provide faster convergence to near-optimal solutions. The MIRHA framework also includes a local search step for improving the incumbent solutions generated during the multi-start process. It also allows the addition of tailored local search components, like cache (memory) or splitting (divide-and-conquer) techniques, that allow the generation of competitive (near-optimal) solutions. The algorithms designed using the MIRHA framework allows to obtain ¿high-quality¿ solutions to realistic problems in reasonable computing times. Moreover, they tend to use a reduced number of parameters, which makes them simple to implement and configure in most practical applications. This framework has successfully been applied in many routing and scheduling problems. One of the main goals of this thesis is to develop new algorithms, based in the aforementioned framework, for solving some combinatorial optimization problems that can be of interest in the telecommunication industry.
Language: English
URI: http://hdl.handle.net/10609/43881
Appears in Collections:Doctoral Thesis

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