Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Automatic Control Systems
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A hybrid genetic algorithm and bacterial foraging approach for global optimization
Information Sciences: an International Journal
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
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation
IEEE Transactions on Evolutionary Computation
Stability analysis of the particle dynamics in particle swarm optimizer
IEEE Transactions on Evolutionary Computation
Stability analysis of the reproduction operator in bacterial foraging optimization
Theoretical Computer Science
Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions
Information Sciences: an International Journal
Swine Influenza Models Based Optimization (SIMBO)
Applied Soft Computing
Controller parameter optimization for nonlinear systems using enhanced bacteria foraging algorithm
Applied Computational Intelligence and Soft Computing
A crossover bacterial foraging optimization algorithm
Applied Computational Intelligence and Soft Computing
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
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 paper, we first 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 indicates the necessary bounds on the chemotactic step-height parameter that avoids limit cycles and guarantees convergence of the bacterial dynamics into an isolated optimum. Illustrative examples as well as simulation results have been provided in order to support the analytical treatments.