Abstract The Equilibrium Optimiser (EO) has been demonstrated to be one of the
metaheuristic algorithms that can effectively solve global optimisation problems. Balancing …

Equilibrium Optimizer (EO) is a novel meta-heuristic algorithm proposed in 2020 and it has a
unique search mechanism and good optimization performance. To further improve its …

The equilibrium optimizer (EO) is a novel physics-based meta-heuristic optimization
algorithm that is inspired by estimating dynamics and equilibrium states in controlled volume …

Background Improvement on the updating equation of an algorithm is among the most
improving techniques. Due to the lack of search ability, high computational complexity and …

Effective machine learning relies on feature selection (FS) to preprocess the data to search
for the best feature subset among all feature combinations, which is a global optimization …

Equilibrium optimizer (EO) is a novel metaheuristic algorithm that exhibits superior
performance in solving global optimization problems, but it may encounter drawbacks such …

Equilibrium Optimizer (EO) is a newly developed physics-based metaheuristic algorithm that
is based on control volume mass balance models, and has shown competitive performance …

Equilibrium optimizer (EO) is a recently proposed physics-based algorithm inspired by the
control volume mass balance model. Although it provides satisfactory solutions for several …

The equilibrium optimizer (EO) is a recently proposed physics-based metaheuristic
algorithm inspired by the dynamic mass balance on a control volume. However, EO may …

Big Data is impacting and changing the way we live, and its core lies in the use of machine
learning to extract valuable information from huge amounts of data. Optimization problems …