This paper proposes a novel meta-heuristic algorithm, named DHOA, which is inspired by
the hunting behavior of humans toward deer. Even though the activities of the hunters differ …

This paper proposes a novel nature-inspired optimization algorithm called the Fox optimizer
(FOX) which mimics the foraging behavior of foxes in nature when hunting preys. The …

In this paper, the Red Deer algorithm (RDA), a recent population-based meta-heuristic
algorithm, is thoroughly reviewed. The RD algorithm combines the survival of the fittest …

The grey wolf optimizer (GWO) is an effective meta-heuristic algorithm. However, since the
update of the search agent's position often depends on the alpha wolf, it is easy to fall into a …

Optimization is a buzzword, whenever researchers think of engineering problems. This
paper presents a new metaheuristic named dingo optimizer (DOX) which is motivated by the …

Abstract The Grey Wolf Optimizer (GWO) is a recently developed population-based meta-
heuristics algorithm that mimics the leadership hierarchy and hunting mechanism of grey …

This paper proposes a new nature-based metaheuristic algorithm called Fennec Fox
Optimization (FFA), mimicking two natural behaviors of the animal Fennec Fox in nature …

Metaheuristic algorithms are intelligent optimization approaches that lead the searching
procedure through utilizing exploitation and exploration. The increasing complexity of real …

This chapter describes the grey wolf optimization (GWO) algorithm as one of the new meta-
heuristic algorithms. First, a brief literature review is presented and then the natural process …

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 …