Discussion of article "Population optimization algorithms: Bacterial Foraging Optimization (BFO)"
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New article Population optimization algorithms: Bacterial Foraging Optimization (BFO) has been published:
E. coli bacterium foraging strategy inspired scientists to create the BFO optimization algorithm. The algorithm contains original ideas and promising approaches to optimization and is worthy of further study.
The Bacterial Foraging Optimization (BFO) algorithm is a fascinating optimization technique that can be used to find approximate solutions to extremely complex or impossible numerical function maximization/minimization problems. The algorithm is widely recognized as a global optimization algorithm for distributed optimization and control. BFO is inspired by the social foraging behavior of Escherichia coli. BFO has already attracted the attention of researchers for its effectiveness in solving real-world optimization problems that arise in several application areas. The biology behind the foraging strategy of E. coli is emulated in an original way and used as a simple optimization algorithm.
Bacteria, such as E. coli or salmonella, are among the most successful organisms on the planet. These agile bacteria have semi-rigid appendages called flagella, with which they propel themselves with a twisting motion. When all the flagella rotate counterclockwise, a propeller effect is created and the bacterium will move in a more or less rectilinear direction. In this case, the bacterium performs a movement called swimming. All flagella rotate in the same direction.
Fig. 1. Replication: division into original (preservation of the motion vector) and cloned (change in the motion vector) bacteria.
Tumble - a change in the vector of bacterium motion
Author: Andrey Dik