Abstract
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.
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