Optimization based on bacterial chemotaxis

We present an optimization algorithm based on a model of bacterial chemotaxis. The original biological model is used to formulate a simple optimization algorithm, which is evaluated on a set of standard test problems. Based on this evaluation, several features are added to the basic algorithm using evolutionary concepts in order to obtain an improved optimization strategy, called the bacteria chemotaxis (BC) algorithm. This strategy is evaluated on a number of test functions for local and global optimization, compared with other optimization techniques, and applied to the problem of inverse airfoil design. The comparisons show that on average, BC performs similar to standard evolution strategies and worse than evolution strategies with enhanced convergence properties.

[1]  Marco Dorigo,et al.  Ant system for Job-shop Scheduling , 1994 .

[2]  H. Bremermann.  Chemotaxis and optimization , 1974 .

[3]  J. Hess.  Panel Methods in Computational Fluid Dynamics , 1990 .

[4]  M. J. Rimlinger,et al.  Constrained Multipoint Aerodynamic Shape Optimization Using an Adjoint Formulation and Parallel Computers , 1997 .

[5]  H. Berg,et al.  Chemotaxis in Escherichia coli analysed by Three-dimensional Tracking , 1972, Nature.

[6]  N. Hansen,et al.  Convergence Properties of Evolution Strategies with the Derandomized Covariance Matrix Adaptation: T , 1997 .

[7]  L. Darrell Whitley,et al.  Building Better Test Functions , 1995, ICGA.

[8]  R. L. Barron,et al.  Adaptive Flight Control Systems , 1970 .

[9]  J. D. E. Koshland.  Bacterial chemotaxis as a model behavioral system , 1980 .

[10]  J. Costerton,et al.  The involvement of cell-to-cell signals in the development of a bacterial biofilm. , 1998, Science.

[11]  M. J. Rimlinger,et al.  Constrained Multipoint Aerodynamic Shape Optimization Using an Adjoint Formulation and Parallel Computers , 1997 .

[12]  Russell W. Anderson.  Biased Random-Walk Learning: A Neurobiological Correlate to Trial-and-Error , 1993, adap-org/9305002.

[13]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  R. L. Barron.  Self-organizing and learning control systems. , 1968 .

[16]  Rainer Storn,et al.  System design by constraint adaptation and differential evolution , 1999, IEEE Trans. Evol. Comput..

[17]  J. Lackie.  Cell Movement and Cell Behaviour , 1986, Springer Netherlands.

[18]  I. H. Abbott,et al.  Theory of Wing Sections , 1959 .

[19]  F. Dahlquist,et al.  Studies of bacterial chemotaxis in defined concentration gradients. A model for chemotaxis toward L-serine. , 1976, Journal of supramolecular structure.

[20]  D. Brown,et al.  Chemotaxis in Escherichia coli analyzed by three-dimensional tracking. , 1974, Antibiotics and chemotherapy.

[21]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[22]  Hans J. Bremermann,et al.  How the Brain Adjusts Synapses - Maybe , 1991, Automated Reasoning: Essays in Honor of Woody Bledsoe.

[23]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..

[24]  Petros Koumoutsakos,et al.  Optimization Algorithms based on a Model of Bacterial Chemotaxis , 2000 .

[25]  A. N. Mucciardi,et al.  Neuromine Nets as the Basis for the Predictive Component of Robot Brains , 1972 .