Optimization based on bacterial chemotaxis

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Elitism-based compact genetic algorithms (2003, methodology): “In order to investigate the performance on such problems, a circle function [ 28 ] and Schaffer’s binary function [29] are employed.”
Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions (2009, methodology): “The Circles function (Muller et al. 2002) contains multiple concentric circles as the regions of local maxima (Fig.”
Natural Evolution Strategies (2008, methodology): “The culminating algorithm, the covariance matrix adaptation evolution strategy (CMA-ES; Hansen and Ostermeier, 2001), has proven successful in numerous studies (e.g., Friedrichs and Igel, 2005; Muller et al., 2002; Shepherd et al., 2006).”
Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior (2009, methodology): “The first one is the bacterial chemotaxis (BC) algorithm, which was based on a bacterial chemotaxis model [9].”
Detection of multiple source locations using a glowworm metaphor with applications to collective robotics (2005, methodology): “[8] present an optimization algorithm based on a model of bacterial chemotaxis to find solutions to multimodal functions.”