After reading this blog post, I thought it would be a good idea to discuss some new metaheuristics inspired by nature.
1 – Spiral Optimization
Developed by Keiichiro Yasuda and Kenichi Tamura in 2011. They approximated focused spiral phenomena to logarithmic spirals and constructed a new optimization algorithm. Recently they developed n-dimension version.
2- Cuckoo Optimization
Xin-she Yang developed the algorithm in 2009. Inspired by some cuckoo species which lay their eggs in the nests of other birds. Logic is like this:
- Each time each cuckoo lays one egg (solution), and puts it in a randomly chosen nest;
- The best nests with high quality of eggs will carry over to the next generations;
- The number of available host nests is ﬁxed, and a host can discover an alien egg with a probability. In this case, the host bird can either throw the egg away or abandon the nest so as to build a completely new nest in a new location.
3 – Intelligent Water Drops
H. Shah-Hosseini proposed the method in 2007. Algorithm inspired from the fact that natural rivers find almost optimal paths to their destination. These near optimal or optimal paths follow from actions and reactions occurring among the water drops and the water drops with their riverbeds. Applications to can be seen at Hosseini’s website.
4 – Big Bang–Big Crunch
Osman Erol and İbrahim Eksin developed the algorithm in 2005. Algorithm consists of two phases: Big Bang and Big Crunch. In the Big Bang phase random points are generated and they shrink to a single representative point via a center of mass or minimal cost approach in the Big Crunch phase. Big Bang and Big Crunch have similar characteristics in physical cosmology. In order to produce a solution, algorithm should be applied several times sequentially. Authors show that this method is superior to genetic algorithms.
For more details about nature inspired metaheuristics, you can read book written by Xin-She Yang “Nature-Inspired Metaheuristic Algorithms“.
Photo by Hiyashi Haka