Ant Colony Optimization

Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented

Autonomous Flight, Fault, and Energy Management of the Flying Fish Solar-Powered Seaplane.

The Flying Fish autonomous unmanned seaplane is designed and built for persistent ocean surveillance. Solar energy harvesting and always-on autonomous control and guidance are required to achieve unattended long-term operation. This thesis describes the Flying Fish avionics and software systems that enable the system to plan, self-initiate, and autonomously execute drift-flight cycles necessary to maintain a designated watch circle subject to environmentally influenced drift. We first present the avionics and flight software architecture developed for the unique challenges of an autonomous energy-harvesting seaplane requiring the system to be: waterproof, robust over a variety of sea states, and lightweight for flight. Seaplane kinematics and dynamics are developed based on conventional aircraft and watercraft and upon empirical flight test data. These models serve as the basis for development of flight control and guidance strategies which take the form of a cyclic multi-mode guidance protocol that smoothly transitions between nested gain-scheduled proportional-derivative feedback control laws tuned for the trim conditions of each flight mode. A fault-tolerant airspeed sensing system is developed in response to elevated failure rates arising from pitot probe water ingestion in the test environment. The fault-tolerance strategy utilizes sensor characteristics and signal energy to combine redundant sensor measurements in a weighted voting strategy, handling repeated failures, sensor recovery, non-homogenous sensors, and periods of complete sensing failure. Finally, a graph-based mission planner combines models of global solar energy, local ocean-currents, and wind with flight-verified/derived aircraft models to provide an energy-aware flight planning tool. An NP-hard asymmetric multi-visit traveling salesman planning problem is posed that integrates vehicle performance and environment models using energy as the primary cost metric. A novel A* search heuristic is presented to improve search efficiency relative to uniform cost search. A series of cases studies are conducted with surface and airborne goals for various times of day and for multi-day scenarios. Energy-optimal solutions are identified except in cases where energy harvesting produces multiple comparable-cost plans via negative-cost cycles. The always-on cyclic guidance/control system, airspeed sensor fault management algorithm, and the nested-TSP heuristic for A* are all critical innovation required to solve the posed research challenges.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91453/1/eubankrd_1.pd

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Estimating the efficacy of mass rescue operations in ocean areas with vehicle routing models and heuristics

Tese de doutoramento, Estatística e Investigação Operacional (Optimização), Universidade de Lisboa, Faculdade de Ciências, 2018Mass rescue operations (MRO) in maritime areas, particularly in ocean areas, are a major concern for the authorities responsible for conducting search and rescue (SAR) activities. A mass rescue operation can be defined as a search and rescue activity characterized by the need for immediate assistance to a large number of persons in distress, such that the capabilities normally available to search and rescue are inadequate. In this dissertation we deal with a mass rescue operation within ocean areas and we consider the problem of rescuing a set of survivors following a maritime incident (cruise ship, oil platform, ditched airplane) that are drifting in time. The recovery of survivors is performed by nearby ships and helicopters. We also consider the possibility of ships capable of refuelling helicopters while hovering which can extend the range to which survivors can be rescued. A linear binary integer formulation is presented along with an application that allows users to build instances of the problem. The formulation considers a discretization of time within a certain time step in order to assess the possibility of travelling along different locations. The problem considered in this work can be perceived as an extension of the generalized vehicle routing problem (GVRP) with a profit stance since we may not be able to recover all of the survivors. We also present a look ahead approach, based on the pilot method, to the problem along with some optimal results using state of the art Mixed-integer linear programming solvers. Finally, the efficacy of the solution from the GVRP is estimated for a set of scenarios that combine incident severity, location, traffic density for nearby ships and SAR assets availability and location. Using traffic density maps and the estimated MRO efficacy, one can produce a combined vulnerability map to ascertain the quality of response to each scenario.Marinha Portuguesa, Plano de Atividades de Formação Nacional (PAFN

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Compressive Sensing Methods and Applications for Electron Microscopy

Scanning Transmission Electron Microscopy (STEM) is at the forefront of high resolution material characterisation. Modern STEMs can be outfitted with a wide variety of ancillary equipment that each provides additional support alongside regular imaging capability. A proficient microscopist can use these tools to characterise a number of materials, though many prove difficult and most are incapable of being characterised with the current techniques available - biological, battery, and catalyst materials are, for example, often much too sensitive to image using standard techniques. The limiting factor for most of these materials is that they suffer from beam damage during characterisation - they are destroyed or changed under the electron beam. Many techniques have arisen to aid with imaging these materials, either by reducing the electron dose rate required to perform the necessary characterisation or increasing the material's tolerance for exposure to the electron beam. A secondary limiting factor to STEM analysis is the acquisition speed. Many transient phenomena, such as atoms moving across the surface of a material, happen at such speeds that exceed the current capabilities of STEM. Transient phenomena such as drift also act as a resolution limiter which plagues every microscope aiming to perform high resolution imaging. Compressive sensing (CS) STEM is a low-dose imaging technique that reduces the number of pixels required to form STEM images. It accomplishes this by combining subsampling, the act of deliberately forming incomplete images by manipulating the electron beam path, and inpainting, a method of infilling gaps in incomplete images. This technique, however, is still in its infancy with regards to electron imaging. The initial attempts to apply CS to scanning electron microscope modalities has done so successfully, but that success has not been explained or justified in great detail. Validation of compressive sensing for electron microscopy is vital at this stage, as while the technique has shown promise, it has not been rigorously examined. The goals of this thesis are then to develop and use compressive sensing as a low-dose STEM imaging technique, investigate and explain why subsampling and inpainting is beneficial, and apply it to various microscopy techniques with a focus on maximising the output from these techniques