Bacterial Foraging Algorithm with Adaptable Chemotactic Step Size

This paper presents development of a new approach involving adaptable chemotactic step size in bacterial foraging algorithm (BFA). Standard BFA only offers a constant chemotactic step size for all nutrient values. The chemotactic step size can be made adaptive, i.e. the chemotactic step size is changed to follow certain condition. The objective of the paper is to investigate adaptation schemes in the BFA so that the chemotactic step size may change depending on the nutrient value. Three approaches, namely using linear function, quadratic function, and exponential function are presented. In the full BFA algorithm, the three proposed approaches will use the new chemotactic step size instead of constant value. Test results with benchmark functions show that BFA with the proposed adaptable step size mechanisms is able to converge faster to the global optimum than the standard BFA.