Route Optimization Algorithms
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Advances in Optimization Algorithms for Multidisciplinary Engineering Applications: From Classical Methods to AI-Enhanced Solutions
This book is an authoritative compilation of the latest advancements in optimization techniques. This book covers a wide array of methods ranging from classical to metaheuristic to AI-enhanced approaches. The chapters are meticulously selected and organized in three sections—metaheuristics, machine learning and engineering applications. This allows for an in-depth exploration of diverse topics ranging from image processing to feature selection to data clustering, to practical applications like energy optimization, smart grids, healthcare diagnostics, etc. Each chapter delves into the specific algorithms and applications as well as provides ample theoretical insights. Accordingly, this book is ideally suited for undergraduate and postgraduate students in fields such as science, engineering and computational mathematics. It is also an invaluable resource for courses on artificial intelligence, computational intelligence, etc. Researchers and professionals in evolutionary computation, artificial intelligence and engineering will find the material especially useful for advancing their work and exploring new frontiers in optimization.
Route and Operating Optimization of Maritime Vessels Using Machine Learning Techniques
Author: Mohammad Hossein Moradi
language: en
Publisher: Logos Verlag Berlin GmbH
Release Date: 2024-02-02
The shipping industry handles over 90% of the global trade volume and is responsible for approximately 3% of global CO2 emissions. Meanwhile, trade by the shipping industry is expected to increase by up to 130% by 2050 compared to 2008. At the same time, the goal is to reduce Green House Gas (GHG) emissions from the shipping industry to half of the 2008 level by 2050. In support of this goal, this thesis is concerned with a comprehensive approach for optimizing the ship's operation, i.e., an optimization approach that simultaneously involves route selection, energy management, propeller pitch, and engine control. In addition, this thesis also analyses the application of wind propulsion systems. The optimization of the ship's operation is implemented in the form of Reinforcement Learning (RL) methods. The use of RL-based methods to simultaneously optimize various aspects of the ship's trajectory and controls is a novel approach compared to the current state-of-art and embodies this thesis' inherent innovation. The results specifically highlight the importance of parallelizing route optimization with the optimization of other control aspects. Ultimately, it is found that the solution emanating from a purely RL-based approach can be further enhanced when the optimized route, speed, and power profiles are used to perform individual DP-based optimizations on the energy management in a post-processing step.
Nature-Inspired Optimization Algorithms for Cyber-Physical Systems
Cyber-physical systems (CPS) integrate computation, communication, control, and physical elements to achieve shared goals with minimal human intervention, encompassing smart technologies such as cities, cloud computing, and smart grids. As CPS components expand, generating vast amounts of data, they face challenges in areas like resource management, security, computation offloading, and automation, demanding advanced techniques beyond traditional algorithms. Nature-inspired optimization algorithms, drawing on natural phenomena, offer scalable and adaptable solutions for these complex issues, making them essential for addressing CPS challenges efficiently and enhancing their role in our daily lives. Nature-Inspired Optimization Algorithms for Cyber-Physical Systems provides relevant theoretical frameworks and the latest empirical research findings in the area. It explores the nature-inspired optimization algorithms intended to boost the performance of CPS. Covering topics such as ant colony optimization, data analysis, and smart cities, this book is an excellent resource for teaching staff, researchers, academicians, graduate and postgraduate students, and more.