Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin's theory of
evolution and has been studied extensively to solve different areas of optimisation and …

Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …

Engineering design problems are usually large-scale constrained optimization problems,
and metaheuristic algorithms are vital for solving such complex problems. Therefore, this …

Optimization algorithms are one of the effective stochastic methods in solving optimization
problems. In this paper, a new swarm-based algorithm called Northern Goshawk …

In order to effectively schedule railway train delay, an adaptive cooperative co-evolutionary
differential evolution with dynamic hybrid mechanism of the quantum evolutionary algorithm …

Python has become the programming language of choice for research and industry projects
related to data science, machine learning, and deep learning. Since optimization is an …

Accurate modelling of the weather's temporal and spatial impacts on building energy
demand is critical to decarbonizing energy systems. Here we introduce a customizable …

Vibrational spectroscopy is an essential tool in chemical analyses, biological assays, and
studies of functional materials. Over the past decade, various coherent nonlinear vibrational …

Differential evolution is an effective and practical approach that is widely applied for solving
global optimization problems. Nevertheless, its effectiveness and scalability are decreased …

Many problems in science and engineering can be formulated as optimization problems,
subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually …