Abstract
Many optimization algorithms and metaheuristics have been inspired by nature. These algorithms often permit solving a wide range of optimization problems. Most of them were inspired by exceptional or extraordinary animal behaviors. On the contrary, in this chapter, we present Artificial Feeding Birds (AFB), a new metaheuristic inspired by the very trivial behavior of birds searching for food. AFB is very simple, yet efficient, and can be easily adapted to various optimization problems. We present application to unconstrained global nonlinear optimization, with several benchmark functions and the training of Artificial Neural Networks (ANN), and to the resolution of ordering combinatorial optimization problems, with two examples: the traveling salesman problem and the optimization of rainbow boxes (a recent visualization technique for overlapping sets). We compare the results with those produced with Artificial Bee Colony (ABC), Firefly Algorithm (FA), Genetic Algorithm (GA), and Ant Colony Optimization (ACO), showing that AFB gives results equivalent or better than the other metaheuristics. Finally, we discuss the choice of inspiration sources from nature, before concluding.
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Lamy, JB. (2019). Artificial Feeding Birds (AFB): A New Metaheuristic Inspired by the Behavior of Pigeons. In: Shandilya, S., Shandilya, S., Nagar, A. (eds) Advances in Nature-Inspired Computing and Applications. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-96451-5_3
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DOI: https://doi.org/10.1007/978-3-319-96451-5_3
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Keywords
- Birds Feeding
- Nonlinear Global Optimization
- Firefly Algorithm (FA)
- Trivial Behavior
- North Pole Direction
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
