Automatic circle detection on images with an adaptive bacterial foraging algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Stability analysis of the reproduction operator in bacterial foraging optimization
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
An improved bacterial foraging optimization
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Numerical optimization using synergetic swarms of foraging bacterial populations
Expert Systems with Applications: An International Journal
An adaptive bacterial foraging algorithm for fuzzy entropy based image segmentation
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Some researchers have illustrated how individual and groups of bacteria forage for nutrients and to model it as a distributed optimization process, which is called the Bacterial Foraging Optimization (BFOA). One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium, which models a trial solution of the optimization problem. In this article, we analyze the chemotactic step of a one dimensional BFOA in the light of the classical Gradient Descent Algorithm (GDA). Our analysis points out that chemotaxis employed in BFOA may result in sustained oscillation, especially for a flat fitness landscape, when a bacterium cell is very near to the optima. To accelerate the convergence speed near optima we have made the chemotactic step size C adaptive. Computer simulations over several numerical benchmarks indicate that BFOA with the new chemotactic operation shows better convergence behavior as compared to the classical BFOA.