Modelling and solving complex combinatorial optimization problems : quorumcast routing, elementary shortest path, elementary longest path and agricultural land allocation

The feasible solution set of a Combinatorial Optimization Problem (COP) is discrete and finite. Solving a COP is to find optimal solutions in the set of feasible solutions such that the value of a given cost function is minimized or maximized. In the literature, there exist both complete and incomplete methods for solving COPs. The complete (or exact) methods return the optimal solutions with the proof of the optimality, for example the branch-and-cut search. The incomplete methods try to find hight-quality solutions which are as close to the optimal solutions as possible, for example local search. In this thesis we focus on solving four distinct COPs: the Quorumcast Routing Problem (QRP), the Elementary Shortest Path Problem on graphs with negative-cost cycles (ESPP), the Elementary Longest Path Problem on graphs with positive-cost cycles (ELPP), and the Agricultural Land Allocation Problem (ALAP). In order to solve these problems with the complete methods, we use the Branch-and-Infer search, the Branch-and-Cut search, and the Branch-and-Price search. We also solve ALAP by the incomplete methods, such as Local Search, Tabu Search, Constraints-Based Local Search that combine with metaheuristics. The experimental evaluations on well-known benchmarks show that all proposed algorithms for all the first three COPs (QRP, ESPP and ELPP) are better than the-state-the art algorithms. Specially, we describe ALAP, formulate it as a combination of three COPs, and propose several complete and incomplete algorithms for these COPs.(FSA - Sciences de l'ingénieur) -- UCL, 201

Collaborative Caching for efficient and Robust Certificate Authority Services in Mobile Ad-Hoc Networks

Security in Mobile Ad-Hoc Network (MANET) is getting a lot of attention due to its inherent vulnerability to a wide spectrum of attacks. Threats exist in every layer of MANET stack, and different solutions have been adapted for each security problem. Additionally, availability is an important criterion in most MANET solutions, but many security frameworks did not consider it. Public-Key Infrastructure (PKI) is no exception, and its deployment in MANET needs major design and implementation modifications that can fit constraints unique to this environment. Our focus in this dissertation is to adapt and increase the availability of Certificate Authority (CA) services, as a major PKI entity, in MANET. Several attempts have been proposed to deal with the problem of deploying CA in MANET to provide a generic public-key framework, but each either ends up sacrificing system security or availability. Here, the main goal of our work is to provide a solution that addresses performance and security issues of providing MANET-based PKI. Particularly, we would like to maintain the availability of the services provided by CA while keeping the network\u27s packet overhead as low as possible. In this dissertation, we present a MANET-based framework suitable for exchanging public-key certificates by collaborative caching between MANET clients. We show that our system can meet the challenges of providing robust and secure CA services in MANET. Augmented by simulation results, we demonstrate quantitatively the feasibility of our work as we were able to reduce network overhead associated with threshold based CA queries up to 92% as compared to related work in addition to having a very short response time. The dependency on CA servers has been reduced, and the system was able to tolerate as much as two-third inoperative CA servers without noticeable decrease in the service performance

Development of hybrid metaheuristics based on instance reduction for combinatorial optimization problems

113 p.La tesis presentada describe el desarrollo de algoritmos metaheurísticos híbridos, basados en reducción de instancias de problema. Éstos son enfocados en la resolución de problemas de optimización combinatorial. La motivación original de la investigación radicó en lograr, a través de la reducción de instancias de problemas, el uso efectivo de modelos de programación lineal entera (ILP) sobre problemas que dado su tamaño no admiten el uso directo con esta técnica exacta. En este contexto se presenta entre otros desarrollos el framework Construct, Merge, Solve & Adapt (CMSA) para resolución de problemas de optimización combinatorial en general, el cual posteriormente fue adaptado para mejorar el desempeño de otras metaheurísticas sin el uso de modelos ILP. Los algoritmos presentados mostraron resultados que compiten o superan el estado del arte sobre los problemas Minimum Common String Partition (MCSP), Minimum Covering Arborescence (MCA) y Weighted Independent Domination (WID)

Development of hybrid metaheuristics based on instance reduction for combinatorial optimization problems

113 p.La tesis presentada describe el desarrollo de algoritmos metaheurísticos híbridos, basados en reducción de instancias de problema. Éstos son enfocados en la resolución de problemas de optimización combinatorial. La motivación original de la investigación radicó en lograr, a través de la reducción de instancias de problemas, el uso efectivo de modelos de programación lineal entera (ILP) sobre problemas que dado su tamaño no admiten el uso directo con esta técnica exacta. En este contexto se presenta entre otros desarrollos el framework Construct, Merge, Solve & Adapt (CMSA) para resolución de problemas de optimización combinatorial en general, el cual posteriormente fue adaptado para mejorar el desempeño de otras metaheurísticas sin el uso de modelos ILP. Los algoritmos presentados mostraron resultados que compiten o superan el estado del arte sobre los problemas Minimum Common String Partition (MCSP), Minimum Covering Arborescence (MCA) y Weighted Independent Domination (WID)