An Adaptive Bacterial Foraging Algorithm for constrained optimization

Attacking the constrained optimization problems with the meta-heuristics techniques has been a popular research topic during the past decade. In this paper, we propose an Adaptive Bacterial Foraging Algorithm (ABFA) with the good nodes set-based crossover operator. The good nodes set is utilized here for initializing the bacteria popu- lation, constructing the crossover operator, and dispersing the similar individuals in the ABFA. A novel adaptive computational chemotaxis is incorporated into our algorithm as well. A hybrid selection approach based on the Pareto-dominance and tournament selection can effectively retain the best individuals in the population. We examine the proposed ABFA using 11 well-known test functions, and compare its performances with other constrained optimization methods. Three interesting engineering design problems are also used to verify the efficiency of our ABFA. ICIC International