QUORUM SENSING BASED BACTERIAL SWARM OPTIMIZATION ON TEST BENCHMARK FUNCTIONS

The Bacterial swarm optimization is one of the latest optimization technique mainly inspired from the swarm of bacteria. This paper introduces an intelligent Quorum sensing based Bacterial Swarm Optimization (QBSO) technique for testing and validation. The quorum sensing senses the best position of the bacteria by knowing the worst place in search space. By knowing these positions, the best optimal solution is attained. Here in this proposed QBSO algorithm the exploration capability of the bacteria is well improved. The proposed technique is validated on the seven standard benchmark with unimodal and multimodal test function for its feasibility and optimality. The basic swarm based optimization algorithms such as Particle Swarm Optimization, Ant Colony Optimization, Biogeography Based Optimization, Simulated Bee Colony and conventional Bacterial Swarm Optimization with the standard parameters are simulated and associated with the proposed technique. The attained results evidently indicate that the proposed method outperforms from the considered optimization methods. Further, the proposed technique may apply to any engineering problems, especially for complex real time optimization problems

A hybrid of bacterial foraging and differential evolution -based distance of sequences

AbstractIn a previous work we presented a new distance that we called the sigma gram distance, which is used to compute the similarity between two sequences. This distance is based on parameters which we computed through an optimization process that used the artificial bee colony; a bio-inspired optimization algorithm. In this paper we show how a hybrid of two optimization algorithms; bacterial foraging and differential evolution, when used to compute the parameters of the sigma gram distance, can yield better results than those obtained by applying artificial bee colony. This superiority in performance is validated through experiments on the same data sets to which artificial bee colony, on the same optimization problem, was tested

A hybrid bacterial foraging and modified particle swarm optimization for model order reduction

This paper study the model reduction procedures used for the reduction of large-scale dynamic models into a smaller one through some sort of differential and algebraic equations. A confirmed relevance between these two models exists, and it shows same characteristics under study. These reduction procedures are generally utilized for mitigating computational complexity, facilitating system analysis, and thence reducing time and costs. This paper comes out with a study showing the impact of the consolidation between the Bacterial-Foraging (BF) and Modified particle swarm optimization (MPSO) for the reduced order model (ROM). The proposed hybrid algorithm (BF-MPSO) is comprehensively compared with the BF and MPSO algorithms; a comparison is also made with selected existing techniques

Hybrid spiral-dynamic bacteria-chemotaxis algorithm with application to control two-wheeled machines

This paper presents the implementation of the hybrid spiral-dynamic bacteria-chemotaxis (HSDBC) approach to control two different configurations of a two-wheeled vehicle. The HSDBC is a combination of bacterial chemotaxis used in bacterial forging algorithm (BFA) and the spiral-dynamic algorithm (SDA). BFA provides a good exploration strategy due to the chemotaxis approach. However, it endures an oscillation problem near the end of the search process when using a large step size. Conversely; for a small step size, it affords better exploitation and accuracy with slower convergence. SDA provides better stability when approaching an optimum point and has faster convergence speed. This may cause the search agents to get trapped into local optima which results in low accurate solution. HSDBC exploits the chemotactic strategy of BFA and fitness accuracy and convergence speed of SDA so as to overcome the problems associated with both the SDA and BFA algorithms alone. The HSDBC thus developed is evaluated in optimizing the performance and energy consumption of two highly nonlinear platforms, namely single and double inverted pendulum-like vehicles with an extended rod. Comparative results with BFA and SDA show that the proposed algorithm is able to result in better performance of the highly nonlinear systems

Natural search algorithms as a bridge between organisms, evolution, and ecology

The ability to navigate is a hallmark of living systems, from single cells to higher animals. Searching for targets, such as food or mates in particular, is one of the fundamental navigational tasks many organisms must execute to survive and reprod uce. Here, we argue that a recent surge of studies of the proximate mechanisms that underlie search behavior offers a new opportunity to integrate the biophysics and neuroscience of sensory systems with ecological and evolutionary processes, closing a feedback loop that promises exciting new avenues of scientific exploration at the frontier of systems biology. Keywords: sensing; navigation; evolutionary strategy; encounter rates; exploration–exploitationGordon and Betty Moore Foundation (Award GBMF3783

Bacterial Foraging Based Channel Equalizers

A channel equalizer is one of the most important subsystems in any digital communication receiver. It is also the subsystem that consumes maximum computation time in the receiver. Traditionally maximum-likelihood sequence estimation (MLSE) was the most popular form of equalizer. Owing to non-stationary characteristics of the communication channel MLSE receivers perform poorly. Under these circumstances ‘Maximum A-posteriori Probability (MAP)’ receivers also called Bayesian receivers perform better. Natural selection tends to eliminate animals with poor “foraging strategies” and favor the propagation of genes of those animals that have successful foraging strategies since they are more likely to enjoy reproductive success. After many generations, poor foraging strategies are either eliminated or shaped into good ones (redesigned). Logically, such evolutionary principles have led scientists in the field of “foraging theory” to hypothesize that it is appropriate to model the activity of foraging as an optimization process. This thesis presents an investigation on design of bacterial foraging based channel equalizer for digital communication. Extensive simulation studies shows that the performance of the proposed receiver is close to optimal receiver for variety of channel conditions. The proposed receiver also provides near optimal performance when channel suffers from nonlinearities

Bacterial foraging-optimized PID control of a two-wheeled machine with a two-directional handling mechanism

This paper presents the performance of utilizing a bacterial foraging optimization algorithm on a PID control scheme for controlling a five DOF two-wheeled robotic machine with two-directional handling mechanism. The system under investigation provides solutions for industrial robotic applications that require a limited-space working environment. The system nonlinear mathematical model, derived using Lagrangian modeling approach, is simulated in MATLAB/Simulink(®) environment. Bacterial foraging-optimized PID control with decoupled nature is designed and implemented. Various working scenarios with multiple initial conditions are used to test the robustness and the system performance. Simulation results revealed the effectiveness of the bacterial foraging-optimized PID control method in improving the system performance compared to the PID control scheme

Short term complex hydro thermal scheduling using integrated PSO-IBF algorithm

In this article, an integrated evolutionary technique such as particle swarm optimization (PSO) algorithm and improved bacterial foraging algorithm (IBFA) have been developed to provide an optimum solution to the scheduling problem with complex thermal and hydro generating stations. PSO algorithm is framed based on the intelligent behavior of the fish school and a flock of birds and the optimal solution in the multidimensional search region is achieved by assigning a random velocity to each potential solution (called the particle). BFA is designed by following the prey-seeking (chemotactic) nature of E. coli bacteria. This technique is followed in an improved manner to get the convergence rate in dynamic for a hyperspace problem by implementing a chemotactic step in a linearly decreased way instead of the static one. The effectiveness of this integrated algorithm is evaluated by using it in a complex thermal and hydro generating system. In this testing system, multiple numbers of cascaded reservoirs in hydro plants have a time coupling effect and thermal power units have a valve point loading effect. The simulation results indicate its merits by comparing it with other meta-heuristic techniques related to the fuel cost required to generate the thermal power.