Diseño e implementación de una metaheurística híbrida basada en recocido simulado, algoritmos genéticos y teoría de autómatas para la optimización bi-objetivo de problemas combinatorios

En la actualidad solo un trabajo de investigación se ha dedicado al estudio del espacio factible para problemas combinatorios multi-objetivo basándose en la teoría de Autómatas Finitos Deterministas. La Metaheurística de Intercambio Determinista sobre Autómatas (MIDA), permite modelar y describir de manera eficiente el espacio de soluciones factibles de problemas tipo no polinomial complejo (NP-hard), específicamente al Problema del Agente Viajero (TSP) multi-objetivo. La tesis de grado presentada a continuación, está basada en MIDA y su principal aporte es el mejoramiento de los resultados obtenidos por ésta al integrar técnicas clásicas de optimización: Recocido Simulado y Algoritmos Genéticos. Al incluir estás dos técnicas, se busca solucionar problemas cada vez más complejos encontrados en diferentes procesos productivos en la industria, con una amplia gama de aplicaciones.MaestríaMagister en Ingeniería Industria

Adaptive bidding strategies in agent-based combinatorial auctions.

Sui, Xin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (p. 91-97).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- MAS-Based Resource Allocation Problems --- p.1Chapter 1.2 --- Combinatorial Auction As a Solution --- p.4Chapter 1.3 --- Strategy Issues --- p.5Chapter 1.4 --- Structure of This Work --- p.7Chapter 2 --- Combinatorial Auctions and Bidding Strategies --- p.9Chapter 2.1 --- Combinatorial Auctions for Resource Allocation --- p.9Chapter 2.2 --- Bidding Strategies in Combinatorial Auctions --- p.11Chapter 2.2.1 --- Berhault's Strategies --- p.11Chapter 2.2.2 --- An's Strategies --- p.13Chapter 2.2.3 --- Schwind´ةs Strategies --- p.15Chapter 2.2.4 --- Wilenius´ةs Strategy --- p.17Chapter 2.2.5 --- Overview of Previous Strategies --- p.18Chapter 3 --- An Adaptive Bidding Strategy in Static Markets --- p.21Chapter 3.1 --- Basic Concepts --- p.22Chapter 3.2 --- The Core Algorithm --- p.24Chapter 3.3 --- Experimental Evaluation --- p.31Chapter 3.3.1 --- Experiment Setup --- p.32Chapter 3.3.2 --- Experiment Results and Analysis --- p.33Chapter 4 --- An Adaptive Bidding Strategy in Dynamic Markets --- p.38Chapter 4.1 --- Basic Concepts --- p.39Chapter 4.2 --- The Core Algorithm --- p.42Chapter 4.3 --- Experimental Evaluation --- p.48Chapter 4.3.1 --- Experiment Setup --- p.49Chapter 4.3.2 --- Experiment Results and Analysis --- p.51Chapter 5 --- A Q-Learning Based Adaptive Bidding Strategy in Static Mar-kets --- p.59Chapter 5.1 --- An Overview of Q-Learning --- p.60Chapter 5.2 --- Basic Concepts --- p.63Chapter 5.3 --- The Core Algorithm --- p.65Chapter 5.4 --- Experimental Evaluation --- p.70Chapter 5.4.1 --- Experiment Setup --- p.70Chapter 5.4.2 --- Experiment Results and Analysis --- p.72Chapter 6 --- Discussion --- p.82Chapter 6.1 --- Applicability of the Adaptive Strategies --- p.82Chapter 6.2 --- Generalization of the Adaptive Strategies --- p.83Chapter 7 --- Conclusion and Future Work --- p.86Chapter 7.1 --- Conclusion --- p.86Chapter 7.2 --- Future Work --- p.88Bibliography --- p.9