Sambarta Dasgupta

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Bacterial foraging optimization algorithm (BFOA) has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. BFOA is inspired by the social foraging behavior of Escherichia coli. BFOA has already drawn the attention of researchers because of its efficiency in solving real-world optimization(More)
Differential Evolution (DE) has recently emerged as a simple yet very powerful technique for real parameter optimization. This article describes an application of DE for the design of Fractional-Order Proportional-Integral-Derivative (FOPID) Controllers involving fractional order integrator and fractional order differentiator. FOPID controllers’ parameters(More)
This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images without using the conventional Hough transform methods. The proposed algorithm is based on a recently developed swarm intelligence technique, known as the bacterial foraging optimization (BFO). A new objective function has been derived to(More)
In his seminal paper published in 2002, Passino pointed out how individual and groups of bacteria forage for nutrients and how to model it as a distributed optimization process, which he called the Bacterial Foraging Optimization Algorithm (BFOA). One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium that models a trial(More)
Social foraging behavior of Escherichia coli bacteria has recently been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real-world(More)
One of the major driving forces of bacterial foraging optimization algorithm (BFOA) is the reproduction phenomenon of virtual bacteria each of which models one trial solution of the optimization problem. During reproduction, the least healthier bacteria (with a lower accumulated value of the objective function in one chemotactic lifetime) die and the other(More)
This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images. The algorithm is based on a hybrid technique composed of simulated annealing and differential evolution. A new fuzzy objective function has been derived for the edge map of a given image. Minimization of this function with a hybrid annealed(More)
In his seminal paper published in 2002, Passino pointed out how individual and groups of bacteria forage for nutrients and how to model it as a distributed optimization process, which he called the Bacterial Foraging Optimization Algorithm (BFOA). One of the major driving forces of BFOA is the reproduction phenomenon of virtual bacteria each of which models(More)
The social foraging behavior of Escherichia coli bacteria has recently been studied by several researchers to develop a new algorithm for distributed optimization control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, has many features analogous to classical Evolutionary Algorithms (EA). Passino [1] pointed out that the foraging(More)
Very recently bacterial foraging has emerged as a powerful technique for solving optimization problems. In this paper, we introduce a micro-bacterial foraging optimization algorithm, which evolves with a very small population compared to its classical version. In this modified bacterial foraging algorithm, the best bacterium is kept unaltered, whereas the(More)