As the world moves towards industrialization, optimization problems become more
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …

Throughout the centuries, nature has been a source of inspiration, with much still to learn
from and discover about. Among many others, Swarm Intelligence (SI), a substantial branch …

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 …

The novelty of this article lies in introducing a novel stochastic technique named the
Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration …

This paper introduces a new metaheuristic algorithm named the Osprey Optimization
Algorithm (OOA), which imitates the behavior of osprey in nature. The fundamental …

Optimization techniques, particularly meta-heuristic algorithms, are highly effective in
optimizing and enhancing efficiency across diverse models and systems, renowned for their …

An original swarm-based, bio-inspired metaheuristic algorithm, named electric eel foraging
optimization (EEFO) is developed and tested in this work. EEFO draws inspiration from the …

In this paper, a novel nature-inspired meta-heuristic known as Crested Porcupine Optimizer
(CPO) and inspired by various defensive behaviors of crested porcupine (CP) is proposed …

Stochastic optimization methods have gained significant prominence as effective techniques
in contemporary research, addressing complex optimization challenges efficiently. This …