Spiral Bacterial Foraging Optimization method

A biologically-inspired algorithm called “Spiral Bacterial Foraging Optimization” is presented in this article in order to find the global optimum of multi-modal objective functions. The proposed algorithm is simply a multi-agent, gradient-based algorithm, such as steepest descent, which minimizes both the main objective function (local search) and the distance between each agent and a temporary central point (global search). A random jump, normal to the connecting line of each agent to the central point, can produce a vortex around the temporary central point. This random jump is also suitable to cope with premature convergence that is a feature of swarm-based optimization methods. The most important contributions of this algorithm are as follows: First, this algorithm involves a stochastic type of search with a deterministic convergence. Second, as the gradient-based methods are employed, faster convergence is expected in the recent algorithm. And finally, the algorithm can be implemented in parallel fashion in order to decentralize large-scale computation.