We present a general-purpose optimisation algorithm inspired by "run-and-tumble2, the biased random chemotactic swimming strategy used by the bacterium E coli to locate regions of high nutrient concentration. The method uses particles (corresponding to bacteria) that swim through the variable space (corresponding to the attractant concentration profile). By constantly performing temporal comparisons, the particles drift towards the minimum or maximum of the function of interest. We illustrate the use of our method with three simple examples. We also present a discrete version of the algorithm. The new algorithm is expected to be useful in combinatorial optimisation problems involving many variables, where the functional landscape is apparently stochastic and has local minima, but preserves some derivative structure at the mesoscale
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.