The search for powerful optimizers has led to the development of a multitude of
metaheuristic algorithms inspired from all areas. This work focuses on the animal kingdom …

Abstract Machine learning (ML) has been extensively applied to model geohazards, yielding
tremendous success. However, researchers and practitioners still face challenges in …

One of the main issues with heuristics and meta-heuristics is the local optima stagnation
phenomena. It is often called premature convergence, which refers to the assumption of a …

Establishing accurate and reliable models based on the measured data for photo-voltaic
(PV) modules are significant to design, control and evaluate the PV systems. Although many …

Abstract Though the Butterfly Bptimization Algorithm (BOA) has already proved its
effectiveness as a robust optimization algorithm, it has certain disadvantages. So, a new …

In this paper, a new optimization algorithm called hybrid leader-based optimization (HLBO)
is introduced that is applicable in optimization challenges. The main idea of HLBO is to …

Feature selection represents an essential pre-processing step for a wide range of Machine
Learning approaches. Datasets typically contain irrelevant features that may negatively …

The photovoltaic (PV) system has attracted attention in recent years for generating more
power and freer from pollution and being eco-friendly to the environment. Nonetheless, the …

Moth flame optimization (MFO) algorithm is a relatively new nature-inspired optimization
algorithm based on the moth's movement towards the moon. Premature convergence and …

One of the most difficult types of problems computationally is the security-constrained
optimal power flow (SCOPF), a non-convex, nonlinear, large-scale, nondeterministic …