Multi-bacterial foraging optimization for dynamic environments

Dynamic optimization problems exist in several real world areas, where, constraints, and objective function change constantly with time. Several techniques have been established to deal with such problems. Using multi-swarm is one of the most efficient techniques used by different approaches. In this paper we propose a multi-population BFO approach, in which each population follows the basic rules of a standard BFO algorithm with some modifications to adapt it to environment dynamism. Inter-population repulsion mechanism is introduced to track multiple optima simultaneously. Performances of this approach are tested on the dynamic benchmark MPB. Results are then compared to the adapted version of BFO and to some other approaches based on PSO in the literature.