Firefly Algorithm: Recent Advances and Applications

TL;DR

This paper reviews the firefly algorithm, a swarm intelligence metaheuristic, discussing its fundamentals, recent applications, and the importance of balancing exploration and exploitation for optimization.

Contribution

It provides a comprehensive overview of recent advances in the firefly algorithm and compares its effectiveness with other search strategies.

Findings

Firefly algorithm outperforms intermittent search strategy.

Balancing exploration and exploitation is crucial for metaheuristic success.

Analysis of high-dimensional optimization challenges.

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.