This article is a part of a series of articles. Links to previous parts can be found in the appendix section of the article.
Firefly Algorithm
As kids, most of us have marveled at the bioluminescence flashes of fireflies in the summer sky. There are approximately 2000 species of fireflies worldwide, and most of these species produce short, rhythmic flashes. Since each species can have different flashing patterns and rhythms, species can be differentiated based on these flashes. Fascinating. Correct?
What many of us did not know back then was that one of the main functions of this flashing mechanism is as a signaling system to communicate with other fireflies. The attractiveness of a firefly is usually linked to the brightness of its flashes and the timing accuracy of its flashing patterns. Characteristics of firefly flashes are:
- The rate of flashing
- The intensity of the flashes
- Amount of time between periods of flashes
These characteristics form part of the signaling system, and their variation communicates different aspects. Some tropical fireflies can synchronize their flashes, leading to self-organized swarm behavior.
The firefly algorithm has three rules based on real fireflies’ flashing characteristics, with modifications. The key ones are:
- In the algorithm formulation, all fireflies are assumed to be unisex, and they will move toward more attractive and brighter ones regardless of their sex.
- A firefly’s degree of attraction is proportional to its flashes’ brightness, which reduces as the distance from the other firefly increases. If a firefly can not find another, brighter, it will then move randomly.
- A firefly’s brightness or light intensity is determined by the value of the objective function of a given problem.
Applications
Scheduling problems are a naturally good fit to leverage these algorithms. Some examples of applications of these algorithms in this category are:
Application of Firefly Algorithm in Job Shop Scheduling Problem for Minimization of Makespan
Application of Firefly Algorithm in Scheduling
Application of Firefly Algorithm in Job Shop Scheduling
Unlike other swarm intelligence algorithms, you can find plenty of research papers leveraging this algorithm for supply chain and operations, like the examples below. However, one paper that caught my attention was the application of this algorithm in layout optimization. In some of my previous articles in this series, I suggested that some of those algorithms may be extrapolated to warehouse layout optimization. This paper is close to that idea.
Firefly Algorithm for Facility Layout Optimization
Another exciting paper leveraged this algorithm more strategically. The paper below also inserts manufacturing costs into the scheduling and production problem. This is a very classic problem that you will find in most operations research books. This research tweaks the structure and leverages a swarm intelligence algorithm.
Optimization of Production Profits Using The Firefly Algorithm
We will continue our journey of swarm intelligence algorithms in the subsequent article. The article will be published on 12/13.
Appendix
This article is part of a series of articles. Previous parts of this article series can be found here:
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