Dottore di ricerca

ciclo: XXXIII

supervisore: Stefano Lucidi

Titolo della tesi: Variable space transformation techniques and new algorithms for global optimization

Solving a global optimization problem is a hard task. In the chapters of this thesis variable space transformation techniques and new algorithmic approaches are proposed to deal with such hard problems. In the first research investigation some variable space transformation techniques are defined as a tool that can be helpfully integrated in (almost) all algorithm frameworks. In particular the focus will be on piecewise linear and non-linear transformations that allow to tackle the problem advantageously. After introducing the theory, preliminary numerical experiments are reported exploiting the transformations in a simple multi-start framework. The idea is to gather the information obtained during a multi-start approach and to apply a sequence of transformations in the variable space that makes the exploration easier. The aim is to expand the attraction basins of global minimizers shrinking those of the local minima already found. Preliminary considerations are made about the possibility to use these transformations as derivative-free preconditioner. The second research investigation concerns the definition of an efficient algorithm on a wide spectrum of global optimization problems. In particular will be discussed how to do an accurate exploratory geometry and a space search reduction strategy, recently renamed in literature as zoom-in strategy, in a probabilistic algorithm that can speed up significantly the convergence towards better solutions. After introducing the algorithm framework named GABRLS, presented as the winner of the Generalization-based Contest in Global Optimization (GENOPT 2017, [62]), the approach is extended to handle also non-continuous variables. The resulting algorithm has been tested in a real case study of design optimization of electric motor. The case study provides evidences about the promising exploratory geometry of the approach in quickly finding feasible and optimal solutions of a mixed integer constrained problem. In the last research investigation, a new black-box approach is proposed to tackle a real case study of the spare part management problem of a fleet of aircraft. In particular, for this specific type of inventory problem, a black-box model and a tailored global optimization algorithm is defined. The aim is to address the non-linearity of the problem as is, without any decomposition in sub-problem and without any approximation or necessity to check ex post the feasibility of the solution. The main contribution consists of advancing the existing literature for multi-item inventory systems through an enhanced time-effective optimization algorithm tested in a single-echelon system.

Produzione scientifica

11573/1488798 - 2021 - An integer black-box optimization model for repairable spare parts management
Bernabei, Giuseppe; Costantino, Francesco; Palagi, Laura; Patriarca, Riccardo; Romito, Francesco - 01a Articolo in rivista
rivista: INTERNATIONAL JOURNAL OF INFORMATION SYSTEMS AND SUPPLY CHAIN MANAGEMENT (Hershey, PA : IGI Global) pp. 46-68 - issn: 1935-5726 - wos: WOS:000631803300003 (0) - scopus: 2-s2.0-85103596475 (0)

11573/1493447 - 2021 - Exploiting non-linear transformations of the variables space in derivative-free global optimization
Liuzzi, Giampaolo; Lucidi, Stefano; Piccialli, Veronica; Romito, Francesco - 13a Altro ministeriale

11573/1334986 - 2019 - Design Optimization of Synchronous Reluctance Motor for Low Torque Ripple
Credo, Andrea; Cristofari, Andrea; Lucidi, Stefano; Rinaldi, Francesco; Romito, Francesco; Santececca, Marco; Villani, Marco - 02a Capitolo o Articolo
libro: A View of Operations Research Applications in Italy - (978-3-030-25841-2; 978-3-030-25842-9)

11573/1043296 - 2017 - Hybridization and discretization techniques to speed up genetic algorithm and solve GENOPT problems
Romito, Francesco - 04b Atto di convegno in volume
congresso: 11th International Conference on Learning and Intelligent Optimization, LION 2017 (Nizhny Novgorod; Russian Federation)
libro: Learning and Intelligent Optimization - (9783319694030; 978-3-319-69404-7)

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