4 research outputs found
Applied (Meta)-Heuristic in Intelligent Systems
Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems
Advances in Branch-and-Fix methods to solve the Hamiltonian cycle problem in manufacturing optimization
159 p.Esta tesis parte del problema de la optimizaci贸n de la ruta de la herramienta donde se contribuye con unsistema de soporte para la toma de decisiones que genera rutas 贸ptimas en la tecnolog铆a de Fabricaci贸nAditiva. Esta contribuci贸n sirve como punto de partida o inspiraci贸n para analizar el problema del cicloHamiltoniano (HCP). El HCP consiste en visitar todos los v茅rtices de un grafo dado una 煤nica vez odeterminar que dicho ciclo no existe. Muchos de los m茅todos propuestos en la literatura sirven paragrafos no dirigidos y los que se enfocan en los grafos dirigidos no han sido implementados ni testeados.Uno de los m茅todos para resolver el problema es el Branch-and-Fix (BF), un m茅todo exacto que utiliza latranformaci贸n del HCP a un problema continuo. El BF es un algoritmo de ramificaci贸n que consiste enconstruir un 谩rbol de decisi贸n donde en cada v茅rtice dos problemas lineales son resueltos. Este m茅todo hasido testeado en grafos de tama帽o peque帽o y por ello, no se ha estudiado en profundidad las limitacionesque puede presentar. Por ello, en esta tesis se proponen cuatro contribuciones metodol贸gicasrelacionadas con el HCP y el BF: 1) mejorar la enficiencia del BF en diferentes aspectos, 2) proponer unm茅todo de ramificaci贸n global, 3) proponer un m茅todo del BF colapsado, 4) extender el HCP a unescenario multi-objetivo y proponer un m茅todo para resolverlo
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OptPlatform: metaheuristic optimisation framework for solving complex real-world problems
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWe optimise daily, whether that is planning a round trip that visits the most attractions within a given holiday budget or just taking a train instead of driving a car in a rush hour. Many problems, just like these, are solved by individuals as part of our daily schedule, and they are effortless and straightforward. If we now scale that to many individuals with many different schedules, like a school timetable, we get to a point where it is just not feasible or practical to solve by hand. In such instances, optimisation methods are used to obtain an optimal solution. In this thesis, a practical approach to optimisation has been taken by developing an optimisation platform with all the necessary tools to be used by practitioners who are not necessarily familiar with the subject of optimisation. First, a high-performance metaheuristic optimisation framework (MOF) called OptPlatform is implemented, and the versatility and performance are evaluated across multiple benchmarks and real-world optimisation problems. Results show that, compared to competing MOFs, the OptPlatform outperforms in both the solution quality and computation time. Second, the most suitable hardware platform for OptPlatform is determined by an in-depth analysis of Ant Colony Optimisation scaling across CPU, GPU and enterprise Xeon Phi. Contrary to the common benchmark problems used in the literature, the supply chain problem solved could not scale on GPUs. Third, a variety of metaheuristics are implemented into OptPlatform. Including, a new metaheuristic based on Imperialist Competitive Algorithm (ICA), called ICA with Independence and Constrained Assimilation (ICAwICA) is proposed. The ICAwICA was compared against two different types of benchmark problems, and results show the versatile application of the algorithm, matching and in some cases outperforming the custom-tuned approaches. Finally, essential MOF features like automatic algorithm selection and tuning, lacking on existing frameworks, are implemented in OptPlatform. Two novel approaches are proposed and compared to existing methods. Results indicate the superiority of the implemented tuning algorithms within constrained tuning budget environment