The feature selection (FS) problem has occupied a great interest of scientists lately since the
highly dimensional datasets might have many redundant and irrelevant features. FS aims to …

Datasets often include excessive or irrelevant data that affect the performance and
complexity of the machine learning model. Feature selection is one of the most effective …

Abstract The Dwarf Mongoose Optimization (DMO) algorithm, inspired by the behaviors and
foraging patterns of dwarf mongooses, is a recently formulated swarm-based metaheuristic …

Mortality from breast cancer (BC) is among the top causes of cancer death in women. BC
can be effectively treated when diagnosed early, improving the likelihood that a patient will …

This paper introduces a novel Improved Dwarf Mongoose Optimizer (IDMO) based on an
Alpha-Directed Learning Process (ADLP) for dealing with different mathematical benchmark …

This paper introduces a novel metaheuristic algorithm named the opposition-based
cooperation search algorithm with Nelder–Mead (OCSANM). This enhanced algorithm …

The process of data classification involves determining the optimal number of features that
lead to high accuracy. However, feature selection (FS) is a complex task that necessitates …

Dynamic differential annealed optimization (DDAO) is a recently developed physics-based
metaheuristic technique that mimics the classical simulated annealing mechanism …

The pursuit of efficient solutions in optimization has led to the development of metaheuristic
algorithms (MHAs). Some of these algorithms adopt the behaviour of several different …

In response to the problems of strong randomness in initialization, imperfect search
mechanism and tendency to fall into local optimum when dealing with complex optimization …