A new adaptive bacterial swarm algorithm

Bacterial foraging optimizer (BFO) is currently getting more and more popular in the community of researchers, for its effectiveness in solving certain difficult real-world optimization problems. Until now, several hybrid approaches integrating BFO with other meta-heuristics methods has been proposed to improve the convergence speed and accuracy of basic BFO. However, the idea behind these hybrid schemes lies in implementing each meta-heuristic algorithm in turn one by one, thus the potential of the BFO can not be fully explored. In this paper we propose a new adaptive bacterial swarm algorithm, termed as ABSA, in order to further accelerate the convergence speed and enhance the accuracy of the adaptive BFO. Firstly, a novel swarming operation is designed for searching the optimal solutions on each field which is comprised of two or three dimensional space. Then, a novel chemotactic mechanism, which is inspired by the concept of hierarchical particle swarm optimizer with time-varying acceleration coefficients, is proposed for controlling the global search and converging to the global optimum. Empirical simulations over several numerical benchmarks demonstrate the proposed ABSA has shown much better convergence behavior, as compared against other adaptive BFO versions.