Firefly Algorithm Applications in Optimization Problems
Summary
The firefly algorithm is a bio-inspired metaheuristic grounded in the flashing behaviour of fireflies. In this model, candidate solutions are treated as fireflies whose brightness represents objective-function values. Less bright individuals move towards more luminous ones according to an attractiveness function that decays with distance, while a randomisation term ensures diversity. Since its introduction, the firefly algorithm has been adapted and hybridised to address a wide spectrum of optimisation challenges, including continuous and combinatorial problems, engineering design, image processing, parameter estimation in biological models, and control-system tuning. Key developments have focused on balancing global exploration and local exploitation, accelerating convergence, avoiding premature stagnation, and automating parameter selection. Practical applications range from structural design and energy management to semantic segmentation and network routing, underlining the global significance of the approach in delivering robust and computationally efficient solutions to complex, multimodal landscapes.
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Firefly Algorithm Applications in Optimization Problems publication trend
The graph below shows the total number of articles in firefly algorithm applications in optimization problems across all publications each year (not limited to Nature Index journals).
Technical terms
Metaheuristic algorithm: A high-level framework guiding subordinate heuristics to explore solution spaces for complex optimisation problems.
Brightness: A measure of solution quality that influences the movement of fireflies towards promising regions.
Attractiveness: A function of brightness and distance that governs the probability and extent of attraction between fireflies.
Exploration vs Exploitation: Exploration refers to global search for diverse solutions, whereas exploitation denotes intensive local search around the best solutions.
Convergence rate: A metric indicating the speed at which an algorithm approaches an optimal or satisfactory solution.
Hyperparameter: A configuration setting that defines algorithmic behaviour and must be tuned for optimal performance.
References
- Semantic segmentation using Firefly Algorithm-based evolving ensemble deep neural networks. Knowledge-Based Systems (2023).
- A new firefly algorithm with improved global exploration and convergence with application to engineering optimization. Decision Analytics Journal (2022).
- A Fast Firefly Algorithm for Function Optimization: Application to the Control of BLDC Motor. Sensors (2021).
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