ISSN: 0973-7510
E-ISSN: 2581-690X
Bacterial Foraging Optimization (BFO) is a recently developed nature-inspired swarm intelligence algorithm, which is based on the foraging behavior of E. coli bacteria. In order to apply BFO in discrete landscape, a binary version of adaptive BFO (BABFO) algorithm is proposed in this manuscript. Unlike the original BFO algorithm, the proposed BABFO represents a food source as a discrete binary variable and applies adaptive operators to change the foraging trajectories of the individual bacterium. With four mathematical benchmark functions, BABFO is proved to have significantly better performance than the other two successful discrete optimizer, namely the genetic algorithm (GA) and particle swarm optimization (PSO).
Discrete optimization, Bacterial foraging, Swarm intelligence
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