Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …

Metaheuristic algorithms are computational intelligence paradigms especially used for
sophisticated solving optimization problems. This chapter aims to review of all …

This paper proposes a novel population-based optimization algorithm called Sine Cosine
Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random …

Specialized data preparation techniques, ranging from data cleaning, outlier detection,
missing value imputation, feature selection (FS), amongst others, are procedures required to …

This paper gives a review on existing scheduling methodologies developed for process
industries. Above all, the aim of the paper is to focus on the industrial aspects of scheduling …

Research in metaheuristics for combinatorial optimization problems has lately experienced
a noteworthy shift towards the hybridization of metaheuristics with other techniques for …

In the past few decades, metaheuristics have demonstrated their suitability in addressing
complex problems over different domains. This success drives the scientific community …

Flower pollination algorithm (FPA) is a computational intelligence metaheuristic that takes its
metaphor from flowers proliferation role in plants. This paper provides a comprehensive …

The harmony search (HS) algorithm is a relatively new population-based metaheuristic
optimization algorithm. It imitates the music improvisation process where musicians …

This paper performs an unprecedented comparative study of Metaheuristic optimization
frameworks. As criteria for comparison a set of 271 features grouped in 30 characteristics …