Dragonfly Algorithm for Benchmark Mathematical Functions Optimization

In this paper, we study the dragonfly algorithm, an optimization method derived from nature and modeled after the swarming behavior of dragonflies. It was developed by Xin-She Yang and has been used for numerous optimization issues (Yang, Xin-She. A New Metaheuristic Bat-Inspired Algorithm. (2010).). The algorithm effectively explores the search space by imitating dragonfly behaviors, like hunting for prey, fleeing from predators, and swarming. A population of virtual dragonflies serves as a representation of possible solutions to the optimization problem in the dragonfly method. Numerous characteristics, including position and velocity, are unique to each dragonfly. The dragonflies communicate with each other through attraction and repulsion forces, which help them navigate the search space and locate promising regions. The algorithm employs a combination of local and global search strategies. Local search consists of exploring the neighborhood of each dragonfly to improve its solution. The global search is performed by adjusting the position and velocity of the dragonflies based on the collective information obtained from the swarm. By iteratively updating the dragonfly positions and velocities, the algorithm aims to converge to an optimal solution over time. The dragonfly algorithm has proven effective in solving a variety of optimization problems, such as function optimization, engineering design, and data clustering. In general, the Dragonfly Algorithm leverages the collective intelligence of a swarm of virtual dragonflies to solve optimization problems by mimicking the behavior of these insects in nature. This algorithm is like search and optimization algorithms, such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA) and Firefly Algorithm (FA) have proven to be efficient in terms of speed and convergence for this type of problems, so we expect the optimization algorithm to work efficiently applied to intelligent computing optimization problems. We conducted experiments with mathematical functions and tested their effectiveness by comparing them with other similar investigations.