As Earth Observation (EO) missions advance towards Agile Earth Observation Satellites, the complexity of scheduling problems increases, posing challenges for traditional optimization methods. this paper investigates t...
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ISBN:
(数字)9783031774324
ISBN:
(纸本)9783031774317;9783031774324
As Earth Observation (EO) missions advance towards Agile Earth Observation Satellites, the complexity of scheduling problems increases, posing challenges for traditional optimization methods. this paper investigates the potential of a quantum algorithm to address the scheduling problem in EO constellations. In particular, a novel formulation of the satellite constellation optimization problem is proposed, translating it into a Quadratic Unconstrained Binary optimization (QUBO) problem, i.e., compliant with quantum solvers. Penalty functions are incorporated to optimize mission energy consumption. the formulated QUBO problem is then implemented and solved on a real quantum computer (a D-Wave Quantum Annealer). the performance provided by the quantum machine is compared with established classical meta-heuristic solvers like Simulated Annealing and Tabu Search. the results show that the proposed quantum optimization process achieves better results in terms of both solution quality and computational efficiency.
Genetic algorithms are machine learningalgorithms inspired by the theory of natural evolution. By mimicking the process of natural selection and reproduction, genetic algorithms can provide high-quality solutions to ...
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optimization of noisy objective functions involves the process of searching for increasingly better solutions, perhaps the optimal one, while performing function evaluations that are influenced by some uncertainty. Di...
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ISBN:
(数字)9783031774324
ISBN:
(纸本)9783031774317;9783031774324
optimization of noisy objective functions involves the process of searching for increasingly better solutions, perhaps the optimal one, while performing function evaluations that are influenced by some uncertainty. Different types of real-world problems fall into this category and, over time, several algorithms have been proposed to solve them efficiently. One of them is the recent Robust Parameter Searcher (RPS), which uses the Nelder Mead Simplex algorithm with some additional operators that perform multiple evaluations of a tentative solution and compare solutions based on a statistical test. this work further explores some possibilities of new operators, and carries out a computational experiment to analyse the effectiveness of different algorithm versions. the experimental results indicate that the RPS version whose single solution reevaluation limit grows non-linearly and whose comparison operator is based on statistical testing was efficient as a good alternative in dealing with noisy optimization problems with real variables and box-type constraints.
To optimize a music recommendation system, advanced algorithms and big data methods were used. the optimization was conducted to solve the cold-start problem and bias and improve accuracy and user satisfaction. By inc...
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the convergence of machine learning and edge computing has led to the development of scalable solutions that bring computation closer to the data source. However, optimizing machine learning models efficiently for edg...
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Quadruped robots hold immense potential for navigating in unknown environments due to their ability to use individual footholds as well as their increased stability in uneven terrain. However, legged robots often expe...
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ISBN:
(数字)9783031774263
ISBN:
(纸本)9783031774256;9783031774263
Quadruped robots hold immense potential for navigating in unknown environments due to their ability to use individual footholds as well as their increased stability in uneven terrain. However, legged robots often experience limitations due to weight shifts during gait transitions. these weight shifts can cause torque peaks that exceed the capacity of the jointmotors (overdrive torque), which lead to an increased risk of mechanical failure. through the optimization of gait parameters, it is possible to reduce these risks while maximizing performance. this paper presents the use of multi-objective optimizationalgorithms for gait optimization in a simulated quadruped mammal robot within the Pybullet physics engine. the main focus of the study was to compare the performance of NSGA-II, NSGA-III and U-NSGA-III in minimizing overdrive torque while maximizing travel distance. the results showed that the three algorithms solve this problem, although the NSGA-III consistently yields better results in comparison to the other versions of the NSGA algorithm.
the conventional cultural algorithm encounters challenges such as low convergence accuracy and limited effectiveness when applied to innovative environmental art design. this paper proposes an environmental art innova...
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In the field of modern education, digital smart teaching cloud platforms are becoming an important trend in teaching reform. this study aims to explore the design and application of digital smart teaching cloud platfo...
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ISBN:
(纸本)9798400710353
In the field of modern education, digital smart teaching cloud platforms are becoming an important trend in teaching reform. this study aims to explore the design and application of digital smart teaching cloud platforms based on artificial intelligence algorithms. Combining personal experience in educational technology, we analyze the specific applications of AI in personalized teaching, intelligent recommendation systems, and natural language processing. through in-depth case analysis and data research, we find that these platforms not only improve teaching efficiency but also greatly enhance the student learning experience. However, challenges remain in building these platforms, such as data privacy issues and content quality control. this paper provides new ideas and suggestions for the future development of smart teaching cloud platforms through detailed examples and unique insights.
Multiway circuit partitioning is a key combinatorial optimization problem that appears many times throughout the Very Large Scale Integration (VLSI) design workflow. However, as VLSI designs continue to grow in size a...
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ISBN:
(数字)9783031774263
ISBN:
(纸本)9783031774256;9783031774263
Multiway circuit partitioning is a key combinatorial optimization problem that appears many times throughout the Very Large Scale Integration (VLSI) design workflow. However, as VLSI designs continue to grow in size and complexity in accordance with Moore's law, current circuit-partitioning algorithms, which are mostly based on simple heuristics that become easily trapped in local minima, are increasingly hard-pressed to produce high-quality solutions in reasonable amounts of CPU runtime. To address this challenge, this paper proposes a novel circuit-partitioning algorithm that combines Deep Reinforcement learning (DRL) withthe popular Fiduccia-Mattheyses-Sanchis (FMS) circuit-partitioning heuristic. A DRL agent is trained to dynamically apply a perturbation function during the search in order to enable FMS escape local minima and to accelerate convergence towards higher-quality solutions. Experimental results obtained show significant improvements both in solution quality and CPU runtime.
In this paper, a comparative analysis of optimizationalgorithms for artificial neural networks widely used in the fields of machine learning and deep learning was conducted. Beyond the conventional backpropagation al...
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