Abstract: The Bacterial Foraging Optimization (BFO) algorithm is inspired by the behavior of bacteria such as Escherichia coli or Myxococcus xanthus when searching for food, more precisely the chemotaxis behavior. Bacteria perceive chemical gradients in the environment, such as nutrients, and also other individual bacteria, and move toward or in the opposite direction to those signals. The application example considered as a case study consists in establishing the dependency between the reaction yield of hydrogels based on polyacrylamide and the working conditions such as time, temperature, monomer, initiator, crosslinking agent and inclusion polymer concentrations, as well as type of the polymer added. This process is modeled with a neural network which is included in an optimization procedure based on BFO. An experimental study of BFO parameters is performed. The results show that the algorithm is quite robust and can obtain good results for diverse combinations of parameter values.
Abstract: Wireless Sensor Networks (WSNs) enable new
applications and need non-conventional paradigms for the protocol
because of energy and bandwidth constraints, In WSN, sensor node’s
life is a critical parameter. Research on life extension is based on
Low-Energy Adaptive Clustering Hierarchy (LEACH) scheme,
which rotates Cluster Head (CH) among sensor nodes to distribute
energy consumption over all network nodes. CH selection in WSN
affects network energy efficiency greatly. This study proposes an
improved CH selection for efficient data aggregation in sensor
networks. This new algorithm is based on Bacterial Foraging
Optimization (BFO) incorporated in LEACH.
Abstract: This paper presents the performance of Integrated
Bacterial Foraging Optimization and Particle Swarm Optimization
(IBFO_PSO) technique in MANET routing. The BFO is a bio-inspired
algorithm, which simulates the foraging behavior of bacteria.
It is effectively applied in improving the routing performance in
MANET. In results, it is proved that the PSO integrated with BFO
reduces routing delay, energy consumption and communication
overhead.
Abstract: This paper is aimed at proposing a rhombus shaped
wearable fractal antenna for wireless communication systems. The
geometrical descriptors of the antenna have been obtained using
bacterial foraging optimization (BFO) for wide band operation. The
method of moment based IE3D software has been used to simulate
the antenna and observed that miniaturization of 13.08% has been
achieved without degrading the resonating properties of the proposed
antenna. An analysis with different substrates has also been done in
order to evaluate the effectiveness of electrical permittivity on the
presented structure. The proposed antenna has low profile, light
weight and has successfully demonstrated wideband and multiband
characteristics for wearable electronic applications.
Abstract: To develop a reliable and cost effective communication platform for the telemedicine applications, novel antenna design has been presented using bacterial foraging optimization (BFO) technique. The proposed antenna geometry is achieved by etching a modified Koch curve fractal shape at the edges and a square shape slot at the center of the radiating element of a patch antenna. It has been found that the new antenna has achieved 43.79% size reduction and better resonating characteristic than the original patch. Representative results for both simulations and numerical validations are reported in order to assess the effectiveness of the developed methodology.