Real-world optimization problems often require an external “modeling engine” that computes fitnesses or data that are then input to an objective function. These programs often have much longer ...

One would imagine that an AI capable of solving the hardest Olympiad problems would naturally produce novel scientific ...

Published in Nature, the study details the first large-scale demonstration of a photonic Ising machine operating without the ...

The unveiling by IBM of two new quantum supercomputers and Denmark's plans to develop "the world's most powerful commercial quantum computer" mark just two of the latest developments in quantum ...

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In the field of multi-objective evolutionary optimization, prior studies have largely concentrated on the scalability of objective functions, with relatively less emphasis on the scalability of ...

1 School of Mathematics and Statistics, Sichuan University of Science and Engineering, Zigong, China. 2 Institute of Computational Mathematics and Scientific/Engineering Computing, Chinese Academy of ...

Abstract: In this article, the distributed form of the zeroing neural network for solving time-varying optimal problems is put forward. Compared with traditional centralized algorithms, distributed ...

It’s been difficult to find important questions that quantum computers can answer faster than classical machines, but a new algorithm appears to do it for some critical optimization tasks. For ...

Abstract: This paper conducts a thorough comparative analysis of optimization algorithms for an unconstrained convex optimization problem. It contrasts traditional methods like Gradient Descent (GD) ...