BioMetaheuristics.jl
This is BioMetaheuristics.jl, an stochastic optimization package that implements a number of metaheuristic algorithms, mostly nature-inspired and bio-inspired.
Reach
This package should/could be used by:
- Practitioners: people in need of a black box optimization framework for when they know very little about the problem at hand.
- Students: wanting to learn about nature-inspired algorithms, stochastic optimization or want a general survey of the current literature.
- Researchers: who want to employ different algorithms at once, test them or use them as comparison for their own developed algorithms.
On Metaheuristics
The term metaheuristic has some history behind it, and a large span of definitions within the scientific community. In BioMetaheuristics.jl, metaheuristic is defined as follows
A metaheuristic is a black box optimization framework that employs heuristics to find a close-to optimal solution for a given optimization problem.
The definition is important in this context. BioMetaheuristics.jl strives on implementations of already existent algorithms, but actually the term itself has not yet been acquired a formal definition, as Sörensen[1] says in his paper.
Because BioMetaheuristics.jl employs metaheuristics as actual black box optimization frameworks, this definition should be enough to provide the actual purpose of the package.
Nature and bio-inspired algorithms
Heuristic is a term for a simple rule. Given a rule, mainly provided by experience, one can create a process or algorithm to solve a given problem.
Nature is an unlimited source of experience that can provide a lot of heuristics for us, if one looks closely.
Recent research has taken this approach[2] and scientists have taken inspiration from nature to create heuristics for problem solving. Examples are Ant Colony Optimization[3], based on the foraging behavior of ant colonies, Particle Swarm Optimization[4], which draws inspiration in the so-called swarm intelligence of birds, people, and so on.
With this mindset, the field of stochastic optimization witnessed an avalanche of "novel" algorithms all based on nature, which were called nature-inspired metaheuristics.
If one takes not only nature, but physics, chemistry and biology, one can create the so-called bio-inspired metaheuristics, based on ideas drawn from biological, chemical and physical processes.
The need for BioMetaheuristics.jl and nature-inspired algorithms
Most metaheuristic algorithms are not very difficult to implement, and most of the time there is already a package for it (e.g. NiaPy for Python).
In general, these algorithms are not consistently used because there are more robust and exact algorithms out there, such as the classics BFGS, L-BFGS, Gradient Descent and many more.
Nonetheless, there are several areas in optimization that need quick and approximate solutions, mostly because they are unsolvable in finite time[1]. Because of this, nature and bio-inspired algorithms rose to the top in some of these problems and were the only framework that could give a reasonable solution.
When there is not much information about the problem (i.e. the derivative or gradient of the fitness function), or if the classic algorithms take too much time to converge, nature and bio-inspired algorithms tackle these types of problems with randomness and heuristics, and having found a close-to optimal solution, more robust algorithms can be applied to the given problem. [5]
About using Julia
In scientific computing, the Julia programming language has been an excellent tool to get rid of the two language problem, i.e. when there is a need for high perfomance calculations but with low level manipulation, problems can be harder to code and even harder to solve. Julia is a great candidate to solve this problem, and this package attempts to prove that by providing high perfomant code and implementations.
The Evolutionary Computation Bestiary
There is a hidden goal for BioMetaheuristics.jl and that is to implement all of the algorithms proposed in the EC Bestiary[6], which is a compilation of most of the nature and bio-inspired algorithms in the literature.
On the name of the package
Because of this hidden goal, the name BioMetaheuristics was chosen, which is a pormanteau of the character by J.K. Rowling, Newton Scamander, whose purpose in life is to collect samples of fantastic beasts and create a book or log of their nature.
References
- 1Sörensen, K. (2015). Metaheuristics-the metaphor exposed. International Transactions in Operational Research, 22(1), 3–18.
- 2Kar, A. K. (2016). Bio inspired computing - A review of algorithms and scope of applications. Expert Systems with Applications, 59, 20–32.
- 3Dorigo, M., & Di Caro, G. (1999, July). Ant colony optimization: a new meta-heuristic. In Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406) (Vol. 2, pp. 1470-1477). IEEE.
- 4Eberhart, R., & Kennedy, J. (1995, November). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (Vol. 4, pp. 1942-1948).
- 5Luke, S. (2011). Essentials of metaheuristics. In Genetic Programming and Evolvable Machines (Vol. 12).
- 6Felipe Campelo, & Claus Aranha. (2018, June 20). EC Bestiary: A bestiary of evolutionary, swarm and other metaphor-based algorithms (Version v2.0.1). Zenodo.