Slime Mould Algorithm (SMA) has recently received much attention from researchers
because of its simple structure, excellent optimisation capabilities, and acceptable …

Meta-heuristic algorithms have a high position among academic researchers in various
fields, such as science and engineering, in solving optimization problems. These algorithms …

In this study, a neuro-fuzzy-based machine learning method is developed to predict the
multiaxial fatigue life of various metallic materials. First, the fuzzy inference system and …

Wind power prediction is crucial for successfully integrating large-scale wind energy with the
grid and achieving a carbon-neutral energy mix. However, previous studies encountered …

Starling murmuration optimizer is a newly well-developed swarm intelligence algorithm
inspired by the behavior of starlings during stunning murmuration and has performed …

Although they are among the most researched real world engineering design problems, it is
encountered with significant problems in the optimization of structural truss bar problems …

As a recent meta-heuristic algorithm, the uniqueness of the grasshopper optimization
algorithm (GOA) is to imitate the biological features of grasshoppers for single-objective …

The greater the demand for energy, the more important it is to improve and develop
permanent energy sources, because of their advantages over non-renewable energy …

Metaheuristic optimization methods; It is a well-known global optimization approach for large-
scale search and optimization problems, commonly used to find the solution many different …

This study presents the mutation–crossover slime mold algorithm (MCSMA) to balance the
time, cost, quality, and work continuity in a particular construction project. The slime mold …