Studying cell metabolism serves a plethora of objectives such as the enhancement of
bioprocess performance, and advancement in the understanding of cell biology, of drug …

Improving the performance of optimization algorithms is a trend with a continuous growth,
powerful and stable algorithms being always in demand, especially nowadays when in the …

This paper presents how an algorithm based on Differential Evolution (DE) with no constant
control parameters solves the dimensional synthesis of four and six-bar mechanisms for …

The determination of the optimal neural network topology is an important aspect when using
neural models. Due to the lack of consistent rules, this is a difficult problem, which is solved …

Pretrained Optimization Models (POMs) leverage knowledge gained from optimizing various
tasks, providing efficient solutions for new optimization challenges through direct usage or …

Differential evolution (DE) is a simple and efficient global optimization algorithm. However,
DE has been shown to have certain weaknesses, especially if the global optimum should be …

Over the past few years, differential evolution (DE) is generally considered as a reliable,
accurate and robust population-based evolutionary algorithm (EA). It is capable of handling …

Component selection in electronic circuit design is an important issue to achieve a targeted
performance and quality level. Particularly in filter circuits, changes in gain and phase …

Differential Evolution (DE), the well-known optimization algorithm, is a tool under the roof of
Evolutionary Algorithms (EAs) for solving non-linear and non-differential optimization …

Asynchronous differential evolution (ADE) supports parallel optimization and effective
exploration. The updation in population is done immediately when a vector with better …