Optimization of Heterogeneous UAV Communications Using the Multiobjective Quadratic Assignment Problem

The Air Force has placed a high priority on developing new and innovative ways to use Unmanned Aerial Vehicles (UAVs). The Defense Advanced Research Projects Agency (DARPA) currently funds many projects that deal with the advancement of UAV research. The ultimate goal of the Air Force is to use UAVs in operations that are highly dangerous to pilots, mainly the suppression of enemy air defenses (SEAD). With this goal in mind, formation structuring of autonomous or semi-autonomous UAVs is of future importance. This particular research investigates the optimization of heterogeneous UAV multi-channel communications in formation. The problem maps to the multiobjective Quadratic Assignment Problem (mQAP). Optimization of this problem is done through the use of a Multiobjective Evolutionary Algorithm (MOEA) called the Multiobjective Messy Genetic Algorithm - II (MOMGA-II). Experimentation validates the attainment of an acceptable Pareto Front for a variety of mQAP benchmarks. It was observed that building block size can affect the location vectors along the current Pareto Front. The competitive templates used during testing perform best when they are randomized before each building block size evaluation. This tuning of the MOMGA-II parameters creates a more effective algorithm for the variety of mQAP benchmarks, when compared to the initial experiments. Thus this algorithmic approach would be useful for Air Force decision makers in determining the placement of UAVs in formations

Dynamic frequency assignment for mobile users in multibeam satellite constellations

Els nivells de flexibilitat i escalabilitat mai vistos de la propera generació de sistemes de comunicació per satèl·lit exigeixen nous algorismes de gestió de recursos que s'adaptin a contextos dinàmics. El futur entorn dels serveis de comunicació per satèl·lit estarà definit per un nombre més gran d'usuaris, una gran part dels quals correspondrà a usuaris mòbils com avions o vaixells. El repte addicional que introdueixen aquests usuaris és abordar la incertesa espai-temporal que es presenta en forma de retards, canvis en la seva trajectòria, o tots dos. Atès que els usuaris mòbils constituiran un segment important del mercat, els operadors de satèl·lits prioritzen l'aprofitament dels avançats sistemes digitals per desenvolupar estratègies flexibles d'assignació de recursos que siguin robustes davant de les bases d'usuaris dinàmiques. Un dels problemes clau en aquest context és com gestionar l'espectre de freqüències de manera eficient. Mentre que nombroses solucions aborden escenaris d'assignació de dinàmica freqüències, el nivell addicional de complexitat que presenten els usuaris mòbils no ha estat prou estudiat, i no és clar si els nous algorismes d'assignació de freqüències poden abordar la incertesa espai-temporal. Concretament, sostenim que els canvis inesperats en la posició dels usuaris introdueixen noves restriccions en l'assignació de freqüències que els algoritmes la literatura podrien no ser capaços de complir, especialment si les decisions s'han de prendre en temps real i a escala. Per solucionar aquesta limitació, proposem un algorisme de gestió dinàmica de freqüències basat en programació lineal entera que assigna recursos a escenaris amb usuaris tant fixos com mòbils, tenint en compte la incertesa espai-temporal d'aquests últims. El nostre mètode inclou tant la planificació a llarg termini com l'operació en temps real, una sinergia que no ha estat prou explorada per a les comunicacions per satèl·lit i que és crítica quan s'opera sota incertesa. PLos niveles de flexibilidad y escalabilidad nunca vistos de la próxima generación de sistemas de comunicación por satélite exigen nuevos algoritmos de gestión de recursos que se adapten a contextos dinámicos. El futuro entorno de los servicios de comunicación por satélite estará definido por un mayor número de usuarios, una gran parte de los cuales corresponderá a usuarios móviles como aviones o barcos. El reto adicional que introducen estos usuarios es abordar la incertidumbre espacio-temporal que se presenta en forma de retrasos, cambios en su trayectoria, o ambos. Dado que los usuarios móviles constituirán un segmento importante del mercado, los operadores de satélites dan prioridad al aprovechamiento de los avanzadas sistemas digitales para desarrollar estrategias flexibles de asignación de recursos que sean robustas frente a las bases de usuarios dinámicas. Uno de los problemas clave en este contexto es cómo gestionar el espectro de frecuencias de forma eficiente. Mientras que numerosas soluciones abordan escenarios de asignación dinámica de frecuencias, el nivel adicional de complejidad que presentan los usuarios móviles no ha sido suficientemente estudiado, y no está claro si los nuevos algoritmos de asignación de frecuencias pueden abordar la incertidumbre espacio-temporal. En concreto, sostenemos que los cambios inesperados en la posición de los usuarios introducen nuevas restricciones en la asignación de frecuencias que los algoritmos la literatura podrían no ser capaces de cumplir, especialmente si las decisiones deben tomarse en tiempo real y a escala. Para solventar esta limitación, proponemos un algoritmo de gestión dinámica de frecuencias basado en la programación lineal entera que asigna recursos en escenarios con usuarios tanto fijos como móviles, teniendo en cuenta la incertidumbre espacio-temporal de estos últimos. Nuestro método incluye tanto la planificación a largo plazo como la operación en tiempo real, una sinergia que no ha sido suficientThe unprecedented levels of flexibility and scalability of the next generation of communication satellite systems call for new resource management algorithms that adapt to dynamic environments. The upcoming landscape of satellite communication services will be defined by an increased number of unique users, a large portion of which will correspond to mobile users such as planes or ships. The additional challenge introduced by these users is addressing the spatiotemporal uncertainty that comes in the form of delays, changes in their trajectory, or both. Given that mobile users will constitute an important segment of the market, satellite operators prioritize leveraging modern digital payloads to develop flexible resource allocation strategies that are robust against dynamic user bases. One of the key problems in this context is how to manage the frequency spectrum efficiently. While numerous solutions address dynamic frequency assignment scenarios, the additional layer of complexity presented by mobile users has not been sufficiently studied, and it is unclear whether novel frequency assignment algorithms can address spatiotemporal uncertainty. Specifically, we argue that unexpected changes in the position of users introduce new restrictions into the frequency assignment, which previous algorithms in the literature might not be able to meet, especially if decisions need to be made in real-time and at scale. To address this gap, we propose a dynamic frequency management algorithm based on integer linear programming that assigns resources in scenarios with both fixed and mobile users, accounting for the spatiotemporal uncertainty of the latter. Our method includes both long-term planning and real-time operation, a synergy that has not been sufficiently explored for satellite communications and proves to be critical when operating under uncertainty. To fulfill the problem’s scope, we propose different strategies that extend a state-of-the-art frequency management algOutgoin

Routing Optimization in Vehicular Networks: A New Approach Based on Multiobjective Metrics and Minimum Spanning Tree

Recently, distributed mobile wireless computing is becoming a very important communications paradigm, due to its flexibility to adapt to different mobile applications. As many other distributed networks, routing operations assume a crucial importance in system optimization, especially when considering dense urban areas, where interference effects cannot be neglected. In this paper a new routing protocol for VANETs and a new scheme of multichannel management are proposed. In particular, an interference-aware routing scheme, for multiradio vehicular networks, wherein each node is equipped with a multichannel radio interface is investigated. NS-2 has been used to validate the proposed Multiobjective routing protocol (MO-RP) protocol in terms of packet delivery ratio, throughput, end-to-end delay, and overhead

Advances and applications in high-dimensional heuristic optimization

“Applicable to most real-world decision scenarios, multiobjective optimization is an area of multicriteria decision-making that seeks to simultaneously optimize two or more conflicting objectives. In contrast to single-objective scenarios, nontrivial multiobjective optimization problems are characterized by a set of Pareto optimal solutions wherein no solution unanimously optimizes all objectives. Evolutionary algorithms have emerged as a standard approach to determine a set of these Pareto optimal solutions, from which a decision-maker can select a vetted alternative. While easy to implement and having demonstrated great efficacy, these evolutionary approaches have been criticized for their runtime complexity when dealing with many alternatives or a high number of objectives, effectively limiting the range of scenarios to which they may be applied. This research introduces mechanisms to improve the runtime complexity of many multiobjective evolutionary algorithms, achieving state-of-the-art performance, as compared to many prominent methods from the literature. Further, the investigations here presented demonstrate the capability of multiobjective evolutionary algorithms in a complex, large-scale optimization scenario. Showcasing the approach’s ability to intelligently generate well-performing solutions to a meaningful optimization problem. These investigations advance the concept of multiobjective evolutionary algorithms by addressing a key limitation and demonstrating their efficacy in a challenging real-world scenario. Through enhanced computational efficiency and exhibited specialized application, the utility of this powerful heuristic strategy is made more robust and evident”--Abstract, page iv