Parallel Ant Colony Algorithm for Shortest Path Problem

During travelling, more and more information must be taken into account, and travelers have to make several complex decisions. In order to support these decisions, IT solutions are unavoidable, and as the computational demand is constantly growing, the examination of state-of-the-art methodologies is necessary. In our research, a parallelized Ant Colony algorithm was investigated, and a parameter study on a real network has been made. The aim was to inspect the sensibility of the method and to demonstrate its applicability in a multi-threaded system (e.g. Cloud-based systems). Based on the research, increased effectiveness can be reached by using more threads. The novelty of the paper is the usage of the processors’ parallel computing capability for routing with the Ant Colony algorithm

EV hitting sets in road networks

As electric vehicles (EVs) become more and more popular, there also has to exist an appropriate infrastructure of battery loading stations to allow for a widespread usage. Especially long distance routes are still not covered, due to the short cruising range of EVs. In this thesis we develop an algorithm for placing such stations so that every shortest path can be driven without running out of energy, assuming an adjustable initial and maximum battery charge. Considering an initial roll-out of battery loading stations, we aim at placing as few as possible, while still meeting the above constraint. Therefore, we rely on a theoretical hitting set formulation of the problem to be able to precisely analyze and evaluate it, followed by a - at first - naive algorithm which is then improved in the course of the thesis. A dual problem is introduced to allow a computation of instance-based bounds. Finally we evaluate our implementation practically in regard to memory usage, runtime and quality of the results and furthermore theoretically prove general upper bounds. The final algorithm is capable of computing a battery loading station positioning on the graph of Germany in less than one day on our testing machine, with evidentially good quality

Combining parallel computing and biased randomization for solving the team orienteering problem in real-time

In smart cities, unmanned aerial vehicles and self-driving vehicles are gaining increased concern. These vehicles might utilize ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to locate their customers and other team vehicles to plan their routes. Furthermore, the team of vehicles should serve their customers by specified due date efficiently. Coordination between the vehicles might be needed to be accomplished in real-time in exceptional cases, such as after a traffic accident or extreme weather conditions. This paper presents the planning of vehicle routes as a team orienteering problem. In addition, an ‘agile’ optimization algorithm is presented to plan these routes for drones and other autonomous vehicles. This algorithm combines an extremely fast biased-randomized heuristic and a parallel computing approach.Peer ReviewedPostprint (published version

Combining Parallel Computing and Biased Randomization for Solving the Team Orienteering Problem in Real-Time

[EN] In smart cities, unmanned aerial vehicles and self-driving vehicles are gaining increased concern. These vehicles might utilize ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to locate their customers and other team vehicles to plan their routes. Furthermore, the team of vehicles should serve their customers by specified due date efficiently. Coordination between the vehicles might be needed to be accomplished in real-time in exceptional cases, such as after a traffic accident or extreme weather conditions. This paper presents the planning of vehicle routes as a team orienteering problem. In addition, an 'agile' optimization algorithm is presented to plan these routes for drones and other autonomous vehicles. This algorithm combines an extremely fast biased-randomized heuristic and a parallel computing approach.This work has been partially supported by the Spanish Ministry of Science and Innovation (PID2019-111100RB-C21/AEI/10.13039/501100011033, RED2018-102642-T). We also acknowledge the support of the Erasmus+ Program (2019-I-ES01-KA103-062602)Panadero, J.; Ammouriova, M.; Juan-Pérez, ÁA.; Agustin, A.; Nogal, M.; Serrat, C. (2021). Combining Parallel Computing and Biased Randomization for Solving the Team Orienteering Problem in Real-Time. Applied Sciences. 11(24):1-18. https://doi.org/10.3390/app112412092118112

A Genetic Algorithm for UAV Routing Integrated with a Parallel Swarm Simulation

This research investigation addresses the problem of routing and simulating swarms of UAVs. Sorties are modeled as instantiations of the NP-Complete Vehicle Routing Problem, and this work uses genetic algorithms (GAs) to provide a fast and robust algorithm for a priori and dynamic routing applications. Swarms of UAVs are modeled based on extensions of Reynolds\u27 swarm research and are simulated on a Beowulf cluster as a parallel computing application using the Synchronous Environment for Emulation and Discrete Event Simulation (SPEEDES). In a test suite, standard measures such as benchmark problems, best published results, and parallel metrics are used as performance measures. The GA consistently provides efficient and effective results for a variety of VRP benchmarks. Analysis of the solution quality over time verifies that the GA exponentially improves solution quality and is robust to changing search landscapes - making it an ideal tool for employment in UAV routing applications. Parallel computing metrics calculated from the results of a PDES show that consistent speedup (almost linear in many cases) can be obtained using SPEEDES as the communication library for this UAV routing application. Results from the routing application and parallel simulation are synthesized to produce a more advanced model for routing UAVs

Exploiting iteration-level parallelism in dataflow programs

The term "dataflow" generally encompasses three distinct aspects of computation - a data-driven model of computation, a functional/declarative programming language, and a special-purpose multiprocessor architecture. In this paper we decouple the language and architecture issues by demonstrating that declarative programming is a suitable vehicle for the programming of conventional distributed-memory multiprocessors.This is achieved by appling several transformations to the compiled declarative program to achieve iteration-level (rather than instruction-level) parallelism. The transformations first group individual instructions into sequential light-weight processes, and then insert primitives to: (1) cause array allocation to be distributed over multiple processors, (2) cause computation to follow the data distribution by inserting an index filtering mechanism into a given loop and spawning a copy of it on all PEs; the filter causes each instance of that loop to operate on a different subrange of the index variable.The underlying model of computation is a dataflow/von Neumann hybrid in that exection within a process is control-driven while the creation, blocking, and activation of processes is data-driven.The performance of this process-oriented dataflow system (PODS) is demonstrated using the hydrodynamics simulation benchmark called SIMPLE, where a 19-fold speedup on a 32-processor architecture has been achieved