Bacterial-inspired algorithms for solving constrained optimization problems

Bio-inspired optimization techniques using analogy of swarming principles and social behavior in nature have been adopted to solve a variety of problems. In this paper Bacterial Foraging Optimization (BFO) is employed to achieve high-quality solutions to the constrained optimization problems. However, the chemotaxis step was set as only a constant in the original BFO, where no mechanism could keep the balance between global search and local search. To further improve the performance of the original BFO, we also come up with two modified BFOs, i.e. BFO with linear decreasing chemotaxis step (BFO-LDC) and BFO with non-linear decreasing chemotaxis step (BFO-NDC). In order to illustrate the efficiency of the proposed method (BFO-LDC and BFO-NDC), six well-known constrained benchmark problems from the optimization literature were selected as testing functions. The experimental results demonstrated that the modified BFOs are of greater efficiency in the speed of convergence as well as fine tune the search in the multidimensional space, and they can be used as a general approach for most nonlinear optimization problems with inequity constrains.