In the last few years, the formulation of real-world optimization problems and their efficient
solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In …

This article is the second part of a two-part survey series on large-scale global optimization.
The first part covered two major algorithmic approaches to large-scale optimization, namely …

This study tenders a new nature-inspired metaheuristic algorithm (MA) based on the
behavior of the Genghis Khan shark (GKS), called GKS optimizer (GKSO), which is used for …

This study proposes the Fire Hawk Optimizer (FHO) as a novel metaheuristic algorithm
based on the foraging behavior of whistling kites, black kites and brown falcons. These birds …

Abstract The Firefly Algorithm (FA) is a highly efficient population-based optimization
technique developed by mimicking the flashing behavior of fireflies when mating. This article …

This paper presents a novel bio-inspired algorithm inspired by starlings' behaviors during
their stunning murmuration named starling murmuration optimizer (SMO) to solve complex …

Nowadays, the design of optimization algorithms is very popular to solve problems in
various scientific fields. The optimization algorithms usually inspired by the natural …

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 …

This paper proposes a novel metaheuristic algorithm called Chimp Optimization Algorithm
(ChOA) inspired by the individual intelligence and sexual motivation of chimps in their group …

This paper proposes a new population-based optimization algorithm called hunter–prey
optimizer (HPO). This algorithm is inspired by the behavior of predator animals such as …