We explain the biology and physics underlying the chemotactic (foraging) behavior of E. coli bacteria. We explain a variety of bacterial swarming and social foraging behaviors and discuss the control system on the E. coli that dictates how foraging should proceed. Next, a computer program that emulates the distributed optimization process represented by the activity of social bacterial foraging is presented. To illustrate its operation, we apply it to a simple multiple-extremum function minimization problem and briefly discuss its relationship to some existing optimization algorithms. The article closes with a brief discussion on the potential uses of biomimicry of social foraging to develop adaptive controllers and cooperative control strategies for autonomous vehicles. For this, we provide some basic ideas and invite the reader to explore the concepts further.

Biomimicry of bacterial foraging for distributed optimization and control

Natural evolution is a population-based optimization process. Simulating this process on a computer results in stochastic optimization techniques that can often outperform classical methods of optimization when applied to difficult real-world problems. There are currently three main avenues of research in simulated evolution: genetic algorithms, evolution strategies, and evolutionary programming. Each method emphasizes a different facet of natural evolution. Genetic algorithms stress chromosomal operators. Evolution strategies emphasize behavioral changes at the level of the individual. Evolutionary programming stresses behavioral change at the level of the species. The development of each of these procedures over the past 35 years is described. Some recent efforts in these areas are reviewed. >

An introduction to simulated evolutionary optimization

Evolutionary algorithms in control systems engineering: a survey

It is shown how genetic algorithms can be applied for system identification of both continuous and discrete time systems. It is shown that they are effective in both domains and are able to directly identify physical parameters or poles and zeros. This can be useful because changing one physical parameter might affect every parameter of a system transfer function. The estimates of poles and zeros are then used to design a discrete time pole placement adaptive controller. Simulations for minimum and nonminimum phase systems and a system with unmodeled dynamics are presented. >

System identification and control using genetic algorithms

http://blog.sciencenet.cn/upload/blog/file/2010/5/201052795823485771.pdf

A hybrid genetic algorithm and bacterial foraging approach for global optimization

Cross-directional control on paper machines using Gram polynomials

Current online identification techniques are recursive and local search techniques. In the present work, it is shown how genetic algorithms, a parallel, global search technique emulating natural genetic operators, can be used to estimate the poles and zeros of a dynamical system. An adaptive controller is designed on the basis of the estimates. Simulations and an experiment show the technique to be satisfactory and to provide unbiased estimates in the presence of colored noise. >

http://read.pudn.com/downloads136/doc/581135/GENETIC%20ALGORITHMS%20IN%20SYSTEM%20IDENTIFICATION.pdf

Genetic algorithms in system identification

Discrete time orthogonal polynomials such as Gram polynomials have been used In the past to control shape on steel rolling mills. In this paper the authors extend that idea to paper machines and control the parameters of the polynomials, taking into account the maximum bending moment of the slice lip and the relative stiffness of the slice with respect to the actuators. >

Paper machine cross directional basis weight control using Gram polynomials

An improved estimation algorithm for use in processing data generated online by a scanning sensor is described. The algorithm is to be used as part of a paper machine control system to maintain the moisture content of the sheet at a target value. The algorithm rapidly estimates, in the presence of noise, cross and machine direction moisture profiles. The basic algorithm consists of a modified least-squares parameter identifier for estimating cross direction profile deviations and a Kalman filter for estimating machine direction disturbances. Simulation results showing the effectiveness of the algorithm in estimating known profiles are given. Results of the offline application of the algorithm to industrial data are also given. Online tests have been performed to demonstrate the improvements in accuracy and speed of detection of process upsets. >

K. Kristinsson

Papers

System identification and control using genetic algorithms

Cross-directional control on paper machines using Gram polynomials

Genetic algorithms in system identification

Paper machine cross directional basis weight control using Gram polynomials

An improved algorithm for estimating paper machine moisture profiles using scanned data