That is the last a part of my series of nature-inspired articles. Earlier, I had talked about algorithms inspired by genetics, swarm, bees, and ants. Today, I’ll discuss wolves.
When a journal paper has a citation count spanning 5 figures, you already know there’s some serious business occurring. Grey Wolf Optimizer [1] (GWO) is one such example.
Like Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO), GWO can be a meta-heuristic. Although there’s no mathematical guarantees to the answer, it really works well in practice and doesn’t require any analytical knowledge of the underlying problem. This permits us to question from a ‘blackbox’, and easily make use to the observed results to refine our solution.
As mentioned in my ACO article, all these ultimately relate back to the elemental concept of explore-exploit trade-off. Why, then, are there so many alternative meta-heuristics?
Firstly, it’s because researchers should publish papers. A very good a part of their job entails exploring things from different angles and sharing the ways by which their findings bring about advantages over existing approaches. (Or as some would say, publishing papers to justify their salaries and seek promotions. But let’s not get there.)
Secondly, it’s attributable to the ‘No Free Lunch’ theorem [2] which the authors of GWO themselves talked about. While that theorem was specifically saying there’s no free lunch for optimization algorithms, I believe it’s fair to say that the identical is true for Data Science usually. There isn’t a single ultimate one-size-fits-all solution, and we frequently should try different approaches to see what works.
Due to this fact, let’s proceed so as to add one more meta-heuristic to our toolbox. Since it never hurts to have one other tool which could turn out to be useful at some point.
First, let’s consider a straightforward classification problem on images. A clever approach is to make use of pre-trained deep neural networks as feature extractors, to convert…