Automatic control systems (5th ed.)
Automatic control systems (5th ed.)
A hybrid genetic algorithm and bacterial foraging approach for global optimization
Information Sciences: an International Journal
Bacterial Foraging Algorithm with Varying Population for Optimal Power Flow
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Transmission loss reduction based on FACTS and bacteria foraging algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A novel model for bacterial foraging in varying environments
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation
IEEE Transactions on Evolutionary Computation
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Stability analysis of the reproduction operator in bacterial foraging optimization
Theoretical Computer Science
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Bacterial Foraging Optimization Algorithm (BFOA) attempts to model the individual and group behavior of E. Coli bacteria as a distributed optimization process. Since its inception, BFOA has been finding many important applications in real-world optimization problems from diverse domains of science and engineering. One key step in BFOA is the computational chemotaxis, where a bacterium (which models a candidate solution of the optimization problem) takes steps over the foraging landscape in order to reach regions with high nutrient content (corresponding to higher fitness). The simulated chemotactic movement of a bacterium may be viewed as a guided random walk or a kind of stochastic hill climbing from the viewpoint of optimization theory. In this article, we firstly derive a mathematical model for the chemotactic movements of an artificial bacterium living in continuous time. The stability and convergence-behavior of the said dynamics is then analyzed in the light of Lyapunov stability theorems. The analysis undertaken provides important insights into the search mechanism of BFOA. In addition, it indicates the necessary bounds on the chemotactic step-height parameter that avoids limit-cycles and guarantees convergence of the bacterial dynamics into an optimum. Illustrative examples as well as simulation results have been provided in order to support the analytical treatments.