最小k部分木問題に対する生物規範型ハイブリッドメタ戦略に基づく近似解法の研究

広島大学(Hiroshima University)博士(工学)Engineeringdoctora

A survey on financial applications of metaheuristics

Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program (E-2015-36)

Low-Diameter Clusters in Network Analysis

In this dissertation, we introduce several novel tools for cluster-based analysis of complex systems and design solution approaches to solve the corresponding optimization problems. Cluster-based analysis is a subfield of network analysis which utilizes a graph representation of a system to yield meaningful insight into the system structure and functions. Clusters with low diameter are commonly used to characterize cohesive groups in applications for which easy reachability between group members is of high importance. Low-diameter clusters can be mathematically formalized using a clique and an s-club (with relatively small values of s), two concepts from graph theory. A clique is a subset of vertices adjacent to each other and an s-club is a subset of vertices inducing a subgraph with a diameter of at most s. A clique is actually a special case of an s-club with s = 1, hence, having the shortest possible diameter. Two topics of this dissertation focus on graphs prone to uncertainty and disruptions, and introduce several extensions of low-diameter models. First, we introduce a robust clique model in graphs where edges may fail with a certain probability and robustness is enforced using appropriate risk measures. With regard to its ability to capture underlying system uncertainties, finding the largest robust clique is a better alternative to the problem of finding the largest clique. Moreover, it is also a hard combinatorial optimization problem, requiring some effective solution techniques. To this aim, we design several heuristic approaches for detection of large robust cliques and compare their performance. Next, we consider graphs for which uncertainty is not explicitly defined, studying connectivity properties of 2-clubs. We notice that a 2-club can be very vulnerable to disruptions, so we enhance it by reinforcing additional requirements on connectivity and introduce a biconnected 2-club concept. Additionally, we look at the weak 2-club counterpart which we call a fragile 2-club (defined as a 2-club that is not biconnected). The size of the largest biconnected 2-club in a graph can help measure overall system reachability and connectivity, whereas the largest fragile 2-club can identify vulnerable parts of the graph. We show that the problem of finding the largest fragile 2-club is polynomially solvable whereas the problem of finding the largest biconnected 2-club is NP-hard. Furthermore, for the former, we design a polynomial time algorithm and for the latter - combinatorial branch-and-bound and branch-and-cut algorithms. Lastly, we once again consider the s-club concept but shift our focus from finding the largest s-club in a graph to the problem of partitioning the graph into the smallest number of non-overlapping s-clubs. This problem cannot only be applied to derive communities in the graph, but also to reduce the size of the graph and derive its hierarchical structure. The problem of finding the minimum s-club partitioning is a hard combinatorial optimization problem with proven complexity results and is also very hard to solve in practice. We design a branch-and-bound combinatorial optimization algorithm and test it on the problem of minimum 2-club partitioning

MISSEL: a method to identify a large number of small species-specific genomic subsequences and its application to viruses classification

Continuous improvements in next generation sequencing technologies led to ever-increasing collections of genomic sequences, which have not been easily characterized by biologists, and whose analysis requires huge computational effort. The classification of species emerged as one of the main applications of DNA analysis and has been addressed with several approaches, e.g., multiple alignments-, phylogenetic trees-, statistical- and character-based methods

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

An investigation of models for identifying critical components in a system.

Lai, Tsz Wai.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (leaves 193-207).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Contributions --- p.2Chapter 1.3 --- Organization --- p.2Chapter 2 --- Literature Review --- p.4Chapter 2.1 --- Taxonomy --- p.4Chapter 2.2 --- Design of Infrastructure --- p.6Chapter 2.2.1 --- Facility Location Models --- p.7Chapter 2.2.1.1 --- Random Breakdowns --- p.7Chapter 2.2.1.2 --- Deliberate Attacks --- p.8Chapter 2.2.2 --- Network Design Models --- p.9Chapter 2.3 --- Protection of Existing Components --- p.10Chapter 2.3.1 --- Interdiction Models --- p.11Chapter 2.3.2 --- Facility Location Models --- p.12Chapter 2.3.2.1 --- Random Breakdowns --- p.12Chapter 2.3.2.2 --- Deliberate Attacks --- p.12Chapter 2.3.3 --- Network Design Models --- p.14Chapter 3 --- Identifying Critical Facilities: Median Problem --- p.16Chapter 3.1 --- Introduction --- p.16Chapter 3.2 --- Problem Formulation --- p.18Chapter 3.2.1 --- The p-Median Problem --- p.18Chapter 3.2.1.1 --- A Toy Example --- p.19Chapter 3.2.1.2 --- Problem Definition --- p.21Chapter 3.2.1.3 --- Mathematical Model --- p.22Chapter 3.2.2 --- The r-Interdiction Median Problem --- p.24Chapter 3.2.2.1 --- The Toy Example --- p.24Chapter 3.2.2.2 --- Problem Definition --- p.27Chapter 3.2.2.3 --- Mathematical Model --- p.28Chapter 3.2.3 --- The r-Interdiction Median Problem with Fortification --- p.29Chapter 3.2.3.1 --- The Toy Example --- p.30Chapter 3.2.3.2 --- Problem Definition --- p.32Chapter 3.2.3.3 --- Mathematical Model --- p.33Chapter 3.2.4 --- The r-Interdiction Median Problem with Fortification (Bilevel Formulation) --- p.35Chapter 3.2.4.1 --- Mathematical Model --- p.36Chapter 3.3 --- Solution Methodologies --- p.38Chapter 3.3.1 --- Model Reduction --- p.38Chapter 3.3.2 --- Variable Consolidation --- p.40Chapter 3.3.3 --- Implicit Enumeration --- p.45Chapter 3.4 --- Results and Discussion --- p.48Chapter 3.4.1 --- Data Sets --- p.48Chapter 3.4.1.1 --- Swain --- p.48Chapter 3.4.1.2 --- London --- p.49Chapter 3.4.1.3 --- Alberta --- p.49Chapter 3.4.2 --- Computational Study --- p.50Chapter 3.4.2.1 --- The p-Median Problem --- p.50Chapter 3.4.2.2 --- The r-Interdiction Median Problem --- p.58Chapter 3.4.2.3 --- The r-Interdiction Median Problem with Fortification --- p.63Chapter 3.4.2.4 --- The r-Interdiction Median Problem with Fortification (Bilevel Formulation) --- p.68Chapter 3.5 --- Summary --- p.76Chapter 4 --- Hybrid Approaches --- p.79Chapter 4.1 --- Framework --- p.80Chapter 4.2 --- Tabu Assisted Heuristic Search --- p.81Chapter 4.2.1 --- A Tabu Assisted Heuristic Search Construct --- p.83Chapter 4.2.1.1 --- Search Space --- p.84Chapter 4.2.1.2 --- Initial Trial Solution --- p.85Chapter 4.2.1.3 --- Neighborhood Structure --- p.85Chapter 4.2.1.4 --- Local Search Procedure --- p.86Chapter 4.2.1.5 --- Form of Tabu Moves --- p.88Chapter 4.2.1.6 --- Addition of a Tabu Move --- p.88Chapter 4.2.1.7 --- Maximum Size of Tabu List --- p.89Chapter 4.2.1.8 --- Termination Criterion --- p.89Chapter 4.3 --- Hybrid Simulated Annealing Search --- p.90Chapter 4.3.1 --- A Hybrid Simulated Annealing Construct --- p.91Chapter 4.3.1.1 --- Random Selection of Immediate Neighbor --- p.92Chapter 4.3.1.2 --- Cooling Schedule --- p.93Chapter 4.3.1.3 --- Termination Criterion --- p.94Chapter 4.4 --- Hybrid Genetic Search Algorithm --- p.95Chapter 4.4.1 --- A Hybrid Genetic Search Construct --- p.99Chapter 4.4.1.1 --- Search Space --- p.99Chapter 4.4.1.2 --- Initial Population --- p.100Chapter 4.4.1.3 --- Selection --- p.104Chapter 4.4.1.4 --- Crossover --- p.105Chapter 4.4.1.5 --- Mutation --- p.106Chapter 4.4.1.6 --- New Population --- p.108Chapter 4.4.1.7 --- Termination Criterion --- p.109Chapter 4.5 --- Further Assessment --- p.109Chapter 4.6 --- Computational Study --- p.114Chapter 4.6.1 --- Parameter Selection --- p.115Chapter 4.6.1.1 --- Tabu Assisted Heuristic Search --- p.115Chapter 4.6.1.2 --- Hybrid Simulated Annealing Approach --- p.121Chapter 4.6.1.3 --- Hybrid Genetic Search Algorithm --- p.124Chapter 4.6.2 --- Expected Performance --- p.128Chapter 4.6.2.1 --- Tabu Assisted Heuristic Search --- p.128Chapter 4.6.2.2 --- Hybrid Simulated Annealing Approach --- p.138Chapter 4.6.2.3 --- Hybrid Genetic Search Algorithm --- p.146Chapter 4.6.2.4 --- Overall Comparison --- p.150Chapter 4.7 --- Summary --- p.153Chapter 5 --- A Special Case of the Median Problems --- p.156Chapter 5.1 --- Introduction --- p.157Chapter 5.2 --- Problem Formulation --- p.158Chapter 5.2.1 --- The r-Interdiction Covering Problem --- p.158Chapter 5.2.1.1 --- Problem Definition --- p.159Chapter 5.2.1.2 --- Mathematical Model --- p.160Chapter 5.2.2 --- The r-Interdiction Covering Problem with Fortification --- p.162Chapter 5.2.2.1 --- Problem Definition --- p.163Chapter 5.2.2.2 --- Mathematical Model --- p.164Chapter 5.2.3 --- The r-Interdiction Covering Problem with Fortification (Bilevel Formulation) --- p.167Chapter 5.2.3.1 --- Mathematical Model --- p.168Chapter 5.3 --- Theoretical Relationship --- p.170Chapter 5.4 --- Solution Methodologies --- p.172Chapter 5.5 --- Results and Discussion --- p.175Chapter 5.5.1 --- The r-Interdiction Covering Problem --- p.175Chapter 5.5.2 --- The r-Interdiction Covering Problem with Fortification --- p.178Chapter 5.5.3 --- The r-Interdiction Covering Problem with Fortification (Bilevel Formulation) --- p.182Chapter 5.6 --- Summary --- p.187Chapter 6 --- Conclusion --- p.189Chapter 6.1 --- Summary of Our Work --- p.189Chapter 6.2 --- Future Directions --- p.19