A new modified incomprehensible but intelligible-in-time logics algorithm for modeling solid oxide fuel cell.

This work suggests a new modified incomprehensible but intelligible-in-time logics algorithm with random contraction strategy (RCS) in exploration phase in addition to new exploitation phase (REILA). The proposed REILA is employed to evaluate the best solid oxide fuel cell (SOFC) parameters in order to create its equivalent circuit. The REILA is an enhanced version of the classical algorithm designed to avoid falling in local solution and achieve well balance between exploration and exploitation phases.

Optimal allocation of hybrid PVDG and DSVC devices into distribution grids using a modified NRBO algorithm considering the overcurrent protection characteristics.

The never-ending issue of inadequate energy availability is constantly on the outermost layer. Consequently, an ongoing effort has been made to improve electric power plants and power system configurations. Photovoltaic Distributed Generators (PVDG) and compensators such as Distributed Static Var Compensator (DSVC) are the center of these recent advances.

Atom Search Optimization: a comprehensive review of its variants, applications, and future directions.

The Atom Search Optimization (ASO) algorithm is a recent advancement in metaheuristic optimization inspired by principles of molecular dynamics. It mathematically models and simulates the natural behavior of atoms, with interactions governed by forces derived from the Lennard-Jones potential and constraint forces based on bond-length potentials. Since its inception in 2019, it has been successfully applied to various challenges across diverse fields in technology and science.

Evaluation of modified fire hawk optimizer for new modification in double diode solar cell model.

The evaluation of photovoltaic (PV) model parameters has gained importance considering emerging new energy power systems. Because weather patterns are unpredictable, variations in PV output power are nonlinear and periodic. It is impractical to rely on a time series because traditional power forecast techniques are based on linearity.

Nonlinear FOPID controller design for pressure regulation of steam condenser via improved metaheuristic algorithm.

Shell and tube heat exchangers are pivotal for efficient heat transfer in various industrial processes. Effective control of these structures is essential for optimizing energy usage and ensuring industrial system reliability. In this regard, this study focuses on adopting a fractional-order proportional-integral-derivative (FOPID) controller for efficient control of shell and tube heat exchanger.

Comparative metabolomics analysis of var. sarcodactylis Swingle and Linn. Fruits and leaves cultivated in Egypt in context to their antiviral effects.

A comprehensive study of fruits and leaves extracts of var. Swingle and L. family Rutaceae was accomplished to investigate their antiviral activity along with their zinc oxide nanoparticles formulation (ZnONPs) against the avian influenza H5N1 virus.

Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models.

Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO).

A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering Problems.

The Artificial Electric Field Algorithm (AEFA) stands out as a physics-inspired metaheuristic, drawing inspiration from Coulomb's law and electrostatic force; however, while AEFA has demonstrated efficacy, it can face challenges such as convergence issues and suboptimal solutions, especially in high-dimensional problems. To overcome these challenges, this paper introduces a modified version of AEFA, named mAEFA, which leverages the capabilities of Lévy flights, simulated annealing, and the Adaptive -best Mutation and Natural Survivor Method (NSM) mechanisms. While Lévy flights enhance exploration potential and simulated annealing improves search exploitation, the Adaptive -best Mutation and Natural Survivor Method (NSM) mechanisms are employed to add more diversity.

Metabolomics analysis of var. sarcodactylis (Siebold ex Hoola van Nooten) Swingle and Linn. fruits and leaves in perspective to their antimicrobial potential.

Antimicrobial potential of var. sarcodactylis (Siebold ex Hoola van Nooten) Swingle and L. fruits and leaves extracts CMF, CML, LAF and LAL, respectively were evaluated.

ST-AL: a hybridized search based metaheuristic computational algorithm towards optimization of high dimensional industrial datasets.

The rapid growth of data generated by several applications like engineering, biotechnology, energy, and others has become a crucial challenge in the high dimensional data mining. The large amounts of data, especially those with high dimensions, may contain many irrelevant, redundant, or noisy features, which may negatively affect the accuracy and efficiency of the industrial data mining process. Recently, several meta-heuristic optimization algorithms have been utilized to evolve feature selection techniques for dealing with the vast dimensionality problem.

An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection.

The increasing use of Internet of Things (IoT) applications in various aspects of our lives has created a huge amount of data. IoT applications often require the presence of many technologies such as cloud computing and fog computing, which have led to serious challenges to security. As a result of the use of these technologies, cyberattacks are also on the rise because current security methods are ineffective.

Review of Different Sequence Motif Finding Algorithms.

The DNA motif discovery is a primary step in many systems for studying gene function. Motif discovery plays a vital role in identification of Transcription Factor Binding Sites (TFBSs) that help in learning the mechanisms for regulation of gene expression. Over the past decades, different algorithms were used to design fast and accurate motif discovery tools.