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

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, ...

Bilevel optimisation is a hierarchical framework involving two interdependent decision-making problems, where the solution of the lower-level problem constrains and influences the upper-level ...

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 ...

Incorporating multiple constraints such as task completion time, UAV payload capacity, and flight range into path optimization algorithms allows for more efficient search patterns.

Many engineering challenges come down to the same headache—too many knobs to turn and too few chances to test them. Whether tuning a power grid or designing a safer vehicle, each evaluation can be ...

Conventional quantum algorithms are not feasible for solving combinatorial optimization problems (COPs) with constraints in the operation time of quantum computers. To address this issue, researchers ...

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 ...

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 ...