Firefly Algorithm: Recent Advances and Applications
arXiv:1308.3898 · doi:10.1504/IJSI.2013.055801
Abstract
Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we discuss the optimality associated with balancing exploration and exploitation, which is essential for all metaheuristic algorithms. By comparing with intermittent search strategy, we conclude that metaheuristics such as firefly algorithm are better than the optimal intermittent search strategy. We also analyse algorithms and their implications for higher-dimensional optimization problems.
15 pages
Cited by in corpus (7)
- Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process
- Why the Firefly Algorithm Works?
- A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
- A hyperbolastic type-I diffusion process: Parameter estimation bymeans of the firefly algorithm
- A Three-Phase Artificial Orcas Algorithm for Continuous and Discrete Problems
- PECCO: A Profit and Cost-oriented Computation Offloading Scheme in Edge-Cloud Environment with Improved Moth-flame Optimisation
- Salp Swarm Optimization: a Critical Review