Multi-Objective Genetic Algorithms for the Single Allocation Hub Location Problem

Hub Location Problems play vital economic roles in transportation and telecommunication networks where goods or people must be efficiently transferred from an origin to a destination point whilst direct origin-destination links are impractical. This work investigates the single allocation hub location problem, and proposes a genetic algorithm (GA) approach for it. The effectiveness of using a single-objective criterion measure for the problem is first explored. Next, a multi-objective GA employing various fitness evaluation strategies such as Pareto ranking, sum of ranks, and weighted sum strategies is presented. The effectiveness of the multi-objective GA is shown by comparison with an Integer Programming strategy, the only other multi-objective approach found in the literature for this problem. Lastly, two new crossover operators are proposed and an empirical study is done using small to large problem instances of the Civil Aeronautics Board (CAB) and Australian Post (AP) data sets.

Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms

Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.

Community Detection in Multi-Layer Networks

In the scope of the current thesis we review and analyse networks that are formed by nodes with several attributes. We suppose that different layers of communities are embedded in such networks, besides each of the layers is connected with nodes' attributes. For example, examine one of a variety of online social networks: an user participates in a plurality of different groups/communities – schoolfellows, colleagues, clients, etc. We introduce a detection algorithm for the above-mentioned communities. Normally the result of the detection is the community supplemented just by the most dominant attribute, disregarding others. We propose an algorithm that bypasses dominant communities and detects communities which are formed by other nodes' attributes. We also review formation models of the attributed networks and present a Human Communication Network (HCN) model. We introduce a High School Texting Network (HSTN) and examine our methods for that network.

Single-Molecule Magnets and Multifunctional Molecular Magnetic Materials Based on Polynuclear Metal Complexes

Our work on single molecule magnets and multifunctional magnetic materials is presented in four projects. In the first project we show for first time that heteroatomic-type pseudohalides, such as OCN-, can be employed as structure-directing ligands and ferromagnetic couplers in higher oxidation state metal cluster chemistry. The initial use of cyanato groups in Mn cluster chemistry has afforded structurally interesting MnII/III14 (1) and MnII/III/IV16 (2) clusters in which the end-on bridging cyanates show a preference in binding through their O-atom. The Mn14 compound shows entirely visible out-of-phase alternating currect signals below 5 K and large hysteresis loops below 2 K. Furthermore, the amalgamation of azido groups with the triethanolamine tripodal ligand in manganese carboxylate cluster chemistry has led to the isolation of a new ferromagnetic, high-nuclearity and mixed-valence MnII/III15Na2 (3) cluster with a large ground-state spin value of S = 14. In the second project we demonstrate a new synthetic route to purely inorganic-bridged, transition metal-azido clusters [CoII7 (4) and NiII7 (5)] and coordination polymers [{FeII/III2}n (6)] which exhibit strong ferromagnetic, SMM and long-range magnetic ordering behaviors. We also show that access to such a unique ferromagnetic class of inorganic, N-rich and O-free materials is feasible through the use of Me3SiN3 as the azido-ligand precursor without requiring the addition of any organic chelating/bridging ligand. In the last projects we have tried to bring together molecular magnetism and optics via the synthesis of multifunctional magnetic materials based on 3d- or 4f-metal ions. We decided to approach such challenge from two different directions: firstly, in our third project, by the deliberate replacement of non-emissive carboxylato ligands in known 3d-SMMs with their fluorescent analogues, without perturbing the metal-core structure and SMM properties (complexes 7, 8, and 9). The second route (last project) involves the use of naphthalene or pyridine-based polyalcohol bridging ligands for the synthesis of new polynuclear LnIII metal clusters (Ln = lanthanide) with novel topologies, SMM behaviors and luminescent properties arising from the increased efficiency of the “antenna” organic group. This approach has led us to the isolation of two new families of LnIII8 (complexes 10-13) and LnIII4 (complexes 14-20) clusters.