As biological data volume continues to grow, sequence-based AI is poised to become the dominant discovery layer across pharma ...
GreenScale: Multi-Objective Autoscaling for SLA, Cost, and Energy Efficiency in Cloud-Native Systems
Autoscaling is the primary method to control the performance level and the cost of cloud-native systems, thereby making them ...
In engineering systems design, theoretical deterministic solutions can be hardly applied directly to real-world scenarios. Basically, this is due to manufacturing limitations and environmental ...
ABSTRACT: Multi-objective optimization remains a significant and realistic problem in engineering. A trade-off among conflicting objectives subject to equality and inequality constraints is known as ...
Abstract: In this paper, we address the problem of efficiently computing finite-size approximations of the set of -locally optimal solutions of a given multi-objective optimization problem (MOP). Such ...
ABSTRACT: Visual Sensor Networks (VSNs) focus on capturing data, extracting relevant information, and enabling communication. However, the presence of obstacles affects network efficiency, linking ...
In the fast-evolving field of electronic systems design, engineers are under increasing pressure to deliver innovative, high-performance products within ever ...
Abstract: Dynamic multi-objective optimization problems (DMOPs) are prevalent in many practical applications and have garnered significant attention from both industry and academia, leading to the ...
In International Conference on Evolutionary Multi-objective Optimization. DOI: 10.1007/978-981-96-3538-2_9 [arXiv] The paper introduces an acquisition function for finding the Pareto front of a ...
With the increase of the scale of the micro-grid system, the optimization of microgrid power dispatching becomes a challenging issue. From the perspective of algorithm design, traditional heuristic ...
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