A synergy of differential evolution and bacterial foraging optimization for global optimization

The social foraging behavior of Escherichia coli bacteria has recently been studied by several researchers to develop a new algorithm for distributed optimization control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, has many features analogous to classical Evolutionary Algorithms (EA). Passino [1] pointed out that the foraging algorithms can be integrated in the framework of evolutionary algorithms. In this way BFOA can be used to model some key survival activities of the population, which is evolving. This article proposes a hybridization of BFOA with another very popular optimization technique of current interest called Differential Evolution (DE). The computational chemotaxis of BFOA, which may also be viewed as a stochastic gradient search, has been coupled with DE type mutation and crossing over of the optimization agents. This leads to the new hybrid algorithm, which has been shown to overcome the problems of slow and premature convergence of both the classical DE and BFOA over several benchmark functions as well as real world optimization problems.