Firefly algorithm: recent advances and applications

TLDR

Nature‑inspired metaheuristic algorithms, especially swarm‑intelligence based ones, have attracted much attention over the last decade, and the firefly algorithm, introduced about five years ago, has seen dramatic literature growth across diverse applications. The paper reviews the fundamentals of the firefly algorithm and recent publications, and discusses optimality in balancing exploration and exploitation, essential for all metaheuristics. The review covers the firefly algorithm fundamentals, analyzes its variants, and examines implications for higher‑dimensional optimization problems. Compared with the intermittent search strategy, the firefly algorithm outperforms it, indicating metaheuristics are superior to the optimal intermittent search strategy.

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.