This paper proposes a novel optimization algorithm Varying Population based Bacterial Foraging Algorithm (VPBFA), to solve optimal bidding strategy in a pool based electricity market. In a power market, Generating Companies (suppliers) participate in the bidding process in order to maximize their profits. Each supplier bid strategically for choosing the bidding coefficients to counter the compe...

—In order to solve discrete optimization problems, this paper proposed a discrete bacterial foraging algorithm (DBFA) based on the idea of bacterial foraging optimization (BFO). The proposed DBFA applies binary encoding, and thus has the advantages of solving both discrete and continuous optimization problems. Also, according to sharing experiences of bacterium, we use the rotation step to upd...

The bacterial foraging optimization (BFO) algorithm mimics how bacteria forage over a landscape of nutrients to perform parallel nongradient optimization. In this article, the author provides a tutorial on BFO, including an overview of the biology of bacterial foraging and the pseudo-code that models this process. The algorithms features are briefly compared to those in genetic algorithms, othe...

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 problems arising in several application dom...

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 problems arising in several application dom...

Microarray gene expression data can be analyzed for diagnosis of cancer and its stage. It usually concerns a very large number of variables relative to a small number of observations. This makes application of data mining techniques difficult and so to reduce the data dimensionality some pre-processing technique needs to be used. In this paper dataset used for analysis is about lung cancer cons...