In recent years, several multi-method and multi-operator-based algorithms have been
proposed for solving optimization problems. Generally, their performance is better than other …

The current work presents the numerical solutions of the robotic system in the process of
coronavirus. The stochastic performances using the modeling of Gudermannian neural …

Meta-heuristics are algorithmic frameworks adaptable to complex optimisation problems.
Swarm intelligence represents intelligent models inspired by existing social systems, based …

During past years, multi-operator optimization techniques gained vast attention due to their
flexible structure and self-adaptation. They yielded promising results when compared with …

Although several multi-operator and multi-method approaches for solving optimization
problems have been proposed, their performances are not consistent for a wide range of …

The current predicament of intangible cultural heritage is its vulnerability and rarity, and an
expression that is difficult to construct in the space-time dimension. It is difficult for visitors to …

The present study performs the design of a novel Gudermannian neural networks (GNNs) for
the nonlinear dynamics of prey-predator system (NDPPS). The process of GNNs is applied …

Abstract Recent research on Differential Evolution (DE) algorithm involves creating a new
variant of DE that improves the algorithm's convergence rate and provides necessary …

Many nature-inspired algorithms have been proposed, but only some handle a broad range
of real-world optimization problems. To improve applicability to a more comprehensive …

In recent years, multi-operator and multi-method algorithms have succeeded, encouraging
their combination within single frameworks. Despite promising results, there remains room …