Optimization algorithms and metaheuristics constitute a vital area of computational science, offering robust strategies for tackling complex, multidimensional problems across diverse domains. These ...

Evolutionary algorithms (EAs) represent a class of heuristic optimisation methods inspired by natural selection and Mendelian genetics. They iteratively evolve a population of candidate solutions ...

In the lower part of the figure, it can be seen that L2O leverages on a set of training problem instances from the target optimization problem class to gain knowledge. This knowledge can help identify ...

Electrical distribution systems are characterized by dynamic operating conditions and complex network topologies, which pose significant challenges for the effective deployment of protection schemes.

The development of vehicle components is a lengthy and therefore very costly process. Researchers have developed a method that can shorten the development phase of the powertrain of battery electric ...

Practical Application: The authors propose QFI-Informed Mutation (QIm), a heuristic that adapts mutation probabilities using diagonal QFI entries. QIm outperforms uniform and random-restart baselines, ...

Many experts believe that once quantum computers are big enough and reliable enough to solve useful problems, the most common deployment architecture will be to have them serve as accelerators for ...

Why has analog design been a manual process? How artificial intelligence is improving the EDA process. The use of artificial intelligence (AI) has gained significant traction in many domains of EDA, ...

Discover actionable tips for aligning your content with AI algorithms and boosting your brand’s presence in an AI-driven world. Generative AI is transforming how consumers discover and engage with ...