Bio-inspired optimisation algorithms draw inspiration from natural processes and the adaptive behaviours of living organisms to address high-dimensional, nonlinear and multi-modal problems. By ...

Foundational optimization algorithms are the core driving force behind deep learning, evolving from early stochastic gradient descent (SGD) to the widely adopted Adam family. However, as the scale of ...

Abstract: Metaheuristic algorithms have demonstrated strong effectiveness in solving complex real-world optimization problems. This paper presents two discrete metaheuristic approaches for the ...

Wale says the modern music landscape is forcing artists like him into a nonstop battle with social media and streaming algorithms just to be heard. During his appearance on Million Dollaz Worth of ...

Abstract: To achieve high-precision engineering design and improve the optimization efficiency of beam optical systems, this paper proposes a two-stage optimization method based on intelligent ...

Traditional approaches to analytical method optimization (e.g., univariate and “guess-and-check”) can be time-consuming, costly, and often fail to identify true optima within the parameter space.

Researchers at the University of Illinois Urbana-Champaign and the University of Virginia have developed a new model architecture that could lead to more robust AI systems with more powerful reasoning ...

ABSTRACT: This paper proposes a unique approach to load forecasting using a fast convergent artificial neural network (ANN) and is driven by the critical need for power system planning. The Mazoon ...