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

Optimal design of cascaded control scheme for PV system using BFO algorithm

In this paper presents Bacteria Foraging Optimization (BFO) algorithm based approach to find the optimum design values for the Proportional-Integral (PI) Controllers in cascaded structure is presented. Tuning the values of four PI controllers is very complex when the system is difficult to express in terms of mathematical model due to system nonlinearity. Response surface methodology (RSM) is used to formulate a mathematical design which is required to apply optimization algorithm. To examine the performance of BFO algorithm in obtaining optimum values of multiple PI controllers, a grid connected Photovoltaic (PV) system is chosen. Transient performance of the PI controller with optimum design values is evaluated under grid fault conditions. The system is simulated using PSCAD/EMTDC. Simulation results have shown the validity of the optimal design values obtained from RSM-BFO approach under different disturbances and system parameter variations

A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming, following and random behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the family of AFSA, encompassing the original ASFA and its improvements, continuous, binary, discrete, and hybrid models, as well as the associated applications. A comprehensive survey on the AFSA from its introduction to 2012 can be found in [1]. As such, we focus on a total of {\color{blue}123} articles published in high-quality journals since 2013. We also discuss possible AFSA enhancements and highlight future research directions for the family of AFSA-based models.Comment: 37 pages, 3 figure

Bio-Inspired Algorithms for the Design of Multiple Optimal Power System Stabilizers: SPPSO and BFA

Damping intra-area and interarea oscillations are critical to optimal power flow and stability in a power system. Power system stabilizers (PSSs) are effective damping devices, as they provide auxiliary control signals to the excitation systems of generators. The proper selection of PSS parameters to accommodate variations in the power system dynamics is important and is a challenging task particularly when several PSSs are involved. Two classical bio-inspired algorithms, which are smallpopulation- based particle swarm optimization (SPPSO) and bacterial foraging algorithm (BFA), are presented in this paper for the simultaneous design of multiple optimal PSSs in two power systems. A classical PSO with a small population of particles is called SPPSO in this paper. The SPPSO uses the regeneration concept, introduced in this paper, to attain the same performance as a PSO algorithm with a large population. Both algorithms use time domain information to obtain the objective function for the determination of the optimal parameters of the PSSs. The effectiveness of the two algorithms is evaluated and compared for damping the system oscillations during small and large disturbances, and their robustness is illustrated using the transient energy analysis. In addition, the computational complexities of the two algorithms are also presented

Robust Tuning of Modern Power System Stabilizers using Bacterial Foraging Algorithm

IEEE Std 421.5, revised by the IEEE excitation system subcommittee introduced a new type of power system stabilizer model, the multiband power system stabilizers (IEEE PSS4B). Although it requires two input signals, like the widely used IEEE PSS2B, the underlying principle of the new IEEE PSS4B makes it sharply different. This paper presents a method based on Bacterial Foraging Algorithm (BFA) to simultaneously tune these modern power system stabilizers (PSSs) in multimachine power system. Simulation results of multi-machine power system validate the efficiency of this approach. the proposed method is effective for the tuning of multi-controllers in large power systems

The Application of Improved Bacteria Foraging Algorithm to the Optimization of Aviation Equipment Maintenance Scheduling

Taking the aviation equipment scheduled maintenance as a prototype, this paper improves a bionic global random search algorithm - bacteria foraging optimization algorithm to solve the task-scheduling problem. Inspired by gene mutation, the activity of bacteria is dynamically adjusted to make good bacteria more capable of action. In addition, a bacterial quorum sensing mechanism is established, which allows bacteria to guide their swimming routes by using their peer experience and enhance their global search capability. Its application to the engineering practice can optimize the scheduling of the maintenance process. It is of great application value in increasing the aviation equipment maintenance efficiency and the level of command automation. In addition, it can improve the resource utilization ratio to reduce the maintenance support cost

Balancing a Segway robot using LQR controller based on genetic and bacteria foraging optimization algorithms

A two-wheeled single seat Segway robot is a special kind of wheeled mobile robot, using it as a human transporter system needs applying a robust control system to overcome its inherent unstable problem. The mathematical model of the system dynamics is derived and then state space formulation for the system is presented to enable design state feedback controller scheme. In this research, an optimal control system based on linear quadratic regulator (LQR) technique is proposed to stabilize the mobile robot. The LQR controller is designed to control the position and yaw rotation of the two-wheeled vehicle. The proposed balancing robot system is validated by simulating the LQR using Matlab software. Two tuning methods, genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) are used to obtain optimal values for controller parameters. A comparison between the performance of both controllers GA-LQR and BFO-LQR is achieved based on the standard control criteria which includes rise time, maximum overshoot, settling time and control input of the system. Simulation results suggest that the BFOA-LQR controller can be adopted to balance the Segway robot with minimal overshoot and oscillation frequency

Economic Load Dispatch Using Bacterial Foraging Technique with Particle Swarm Optimization Biased Evolution

This paper presents a novel modified bacterial foraging technique (BFT) to solve economic load dispatch (ELD) problems. BFT is already used for optimization problems, and performance of basic BFT for small problems with moderate dimension and searching space is satisfactory. Search space and complexity grow exponentially in scalable ELD problems, and the basic BFT is not suitable to solve the high dimensional ELD problems, as cells move randomly in basic BFT, and swarming is not sufficiently achieved by cell-to-cell attraction and repelling effects for ELD. However, chemotaxis, swimming, reproduction and elimination-dispersal steps of BFT are very promising. On the other hand, particles move toward promising locations depending on best values from memory and knowledge in particle swarm optimization (PSO). Therefore, best cell (or particle) biased velocity (vector) is added to the random velocity of BFT to reduce randomness in movement (evolution) and to increase swarming in the proposed method to solve ELD. Finally, a data set from a benchmark system is used to show the effectiveness of the proposed method and the results are compared with other methods

Bio inspired techniques for simultaneous design of multiple optimal power system stabilizers

Bio-inspired techniques are fields of study that are inspired from topics of connectionism, social behavior and emergence. Researchers have ventured into the intricacies involved with the techniques and devised algorithms based on their study. Such techniques are the focus of this thesis. The two bio-inspired techniques used for simultaneous design of power system stabilizers (PSSs) in this study are - Particle Swam Optimization (PSO) and Bacteria Foraging Algorithm (BFA). The work in this thesis is presented in three papers as follows: Paper 1 -This paper introduces an improved PSO called Small Population based PSO (SPPSO) with less number of particles and unique regeneration concept. The efficacy of the algorithm is evaluated for the simultaneous design of power system stabilizers (PSSs) on the two-area and 16 machine power systems. Paper 2 - The second paper presents a new algorithm - Bacterial Foraging Algorithm (BFA) for simultaneous tuning of multiple PSSs on a 16 machine power system. The variants of the BFA like the run length and the swarming are explored for better performance for two different design techniques and the results are compared. Paper 3 - The third paper compares SPPSO and BFA towards simultaneous tuning of multiple PSSs on two-area and Nigerian power system. This paper presents both algorithms as a first step towards online optimization and proposes to implement these algorithms in real power systems in near future --Abstract, page iv