Abstract The Mountain Gazelle Optimizer (MGO), a novel meta-heuristic algorithm inspired
by the social life and hierarchy of wild mountain gazelles, is suggested in this paper. In this …

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 …

This paper proposes an efficient optimization algorithm based on the physical phenomenon
of rime-ice, called the RIME. The RIME algorithm implements the exploration and …

In this paper, a novel population-based technique called dung beetle optimizer (DBO)
algorithm is presented, which is inspired by the ball-rolling, dancing, foraging, stealing, and …

In this paper, a new metaheuristic algorithm called the Coati Optimization Algorithm (COA) is
introduced, which mimics coati behavior in nature. The fundamental idea of COA is the …

Dandelion Optimizer (DO) is a recently proposed swarm intelligence algorithm that
coincides with the process of finding the best reproduction site for dandelion seeds …

In this paper, a new bio-inspired meta-heuristic algorithm, named artificial rabbits
optimization (ARO), is proposed and tested comprehensively. The inspiration of the ARO …

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 …

In this paper, a novel swarm-based metaheuristic algorithm inspired from the behaviors of
beluga whales, called beluga whale optimization (BWO), is presented to solve optimization …

Nature-inspired metaheuristic approaches draw their core idea from biological evolution in
order to create new and powerful competing algorithms. Such algorithms can be divided into …