Dragonfly Algorithm Applications in Optimization Problems

The dragonfly algorithm is a nature-inspired metaheuristic that mimics the static and dynamic swarming behaviours of dragonflies in prey hunting and predator evasion. Since its introduction, it has been applied across a wide spectrum of continuous and discrete optimisation tasks. In continuous settings, the algorithm balances exploration and exploitation through position-updating rules that govern attraction to food sources, distraction from enemies and alignment with neighbouring agents. In discrete or binary domains, angle modulation and transfer functions enable the dragonfly algorithm to tackle combinatorial problems such as feature selection and knapsack optimisation. Advances have included hybridisations with chaotic maps, levy flights or other swarm-based methods to enhance convergence speed and to reduce the risk of entrapment in local optima. Practical applications span engineering design, energy-efficient control, robotic path planning, parameter identification in renewable-energy systems and network clustering. Recent work has further demonstrated the algorithm’s adaptability to high-dimensional objectives and its robustness in noisy or dynamic environments. Overall, the dragonfly algorithm has matured into a versatile tool whose global significance lies in its capacity to deliver reliable, low-cost solutions for complex real-world optimisation challenges.

Research from Nature Portfolio

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Research from all publishers

Researchers have proposed a neighbourhood dragonfly algorithm for real-time drone path planning in indoor environments, exploiting collaborative search to negotiate both stationary and moving obstacles. Simulations and physical trials demonstrated path-length savings and timely convergence, positioning this approach as a candidate for robust surveillance and search-and-rescue systems. Another study introduced a hybrid-strategy dragonfly algorithm for parameter identification in solar-cell models. By combining chaotic initialisation, a whale-inspired update mechanism and perturbation strategies, the method achieved high accuracy across multiple benchmark test functions and engineering case studies, notably reducing error in single- and multi-diode circuits. In the field of industrial drive systems, a hybrid dragonfly-based optimisation was developed to tune proportional–integral controllers for induction motors. The algorithm merged levy-flight swarming behaviours with group-search operators, resulting in improved energy efficiency under partial-load conditions and offering potential for significant electricity savings in large-scale manufacturing installations.

Dragonfly Algorithm Applications in Optimization Problems publication trend

The graph below shows the total number of articles in dragonfly algorithm applications in optimization problems across all publications each year (not limited to Nature Index journals).

Technical terms

Swarm intelligence: A collective problem-solving paradigm inspired by the coordinated behaviour of social organisms.

Metaheuristic algorithm: A high-level procedure designed to find, generate or select heuristics that provide sufficiently good solutions to optimisation problems.

Exploration and exploitation: The balance between searching new regions of the solution space (exploration) and refining known high-quality areas (exploitation).

Convergence rate: The speed at which an algorithm approaches its optimal or near-optimal solution.

Fitness function: An objective measure used to evaluate and compare candidate solutions in optimisation.

References

1. A Novel Technique for Drone Path Planning Based on a Neighborhood Dragonfly Algorithm. Sensors (2025).
2. A Hybrid-Strategy-Improved Dragonfly Algorithm for the Parameter Identification of an SDM. Sustainability (2023).
3. Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications. Sensors (2021).
4. A Hybrid Dragonfly Algorithm for Efficiency Optimization of Induction Motors. Sensors (2022).