Realistic problems typically have many conflicting objectives. Therefore, it is instinctive to
look at the engineering problems as multi-objective optimization problems. This paper briefly …

Selecting relevant feature subsets is vital in machine learning, and multiclass feature
selection is harder to perform since most classifications are binary. The feature selection …

This study proposes a new nature-inspired metaheuristic that mimics the behaviour of the
prairie dogs in their natural habitat called the prairie dog optimization (PDO). The proposed …

Moth-flame optimization (MFO) is a prominent problem solver with a simple structure that is
widely used to solve different optimization problems. However, MFO and its variants …

Today, intelligent optimization has become a science that few researchers have not used in
dealing with problems in their field. Diversity and flexibility have made the use, efficiency …

In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm
Optimization (CSCSO) is proposed for constrained and complex optimization problems. This …

The research of mobile robot path planning has shifted from the static environment to the
dynamic environment, from the two-dimensional environment to the high-dimensional …

The Henry Gas Solubility Optimization (HGSO) is a physics-based metaheuristic inspired by
Henry's law, which describes the solubility of the gas in a liquid under specific pressure …

The butterfly optimization algorithm (BOA) is a meta-heuristic algorithm that mimics foraging
and mating behavior of butterflies. In order to alleviate the problems of slow convergence …

The problem regarding the optimal placement and sizing of different FACTS (flexible
alternating current transmission systems) in electrical distribution networks is addressed in …