Adaptive Bacterial Foraging Optimization Research Articles

Fuzzy rules based smart irrigation system using adaptive bacterial foraging optimization

The practice of agriculture is heavily reliant on the implementation of irrigation systems. Technology improvements have made it unnecessary to rely on someone else to perform irrigation when we are out and whenever crops need to be watered. Many researchers have attempted to autonomously irrigate crops, but difficulties with accuracy, timing, and cost are rarely addressed and given top priority. The proposed approach employs a real-time sensor, wireless sensor network, the adaptive bacterial foraging optimization (ABFO) algorithm, and a fuzzy irrigation system control to achieve autonomous watering, thereby enabling smart irrigation. This method reduces waste while preserving the container’s water supply. Automated irrigation determines whether crops need to be watered by considering the type of crop, the weather, and the soil moisture and not soil moisture alone. The need for water is calculated using the three aforementioned criteria and fuzzy control drives the automation. Using an arduino-based IoT circuitry, the bioinspired model with algorithm adaptive bacterial foraging optimization, generates the optimized values for three parameters, which are then used by fuzzy logic control to predict the watering requirements of the plants. In terms of accuracy, timeliness, and cost, the suggested approach is advantageous. With this model, it is now possible to completely automate the irrigation system.

GOKA: A Network Partition and Cluster Fusion Algorithm for Controller Placement Problem in SDN

Software Defined Networking (SDN) is a new promising network architecture, with the property of decoupling the data plane from the control plane and centralizing the network topology logically, making the network more agile than traditional networks. However, with the continuous expansion of network scales, the single-controller SDN architecture is unable to meet the performance requirements of the network. As a result, the logically centralized and physically separated SDN multi-controller architecture comes into being, and thereupon the Controller Placement Problem (CPP) is proposed. In order to minimize the propagation latency in Wide Area Network (WAN), we propose Greedy Optimized K-means Algorithm (GOKA) which combines K-means with greedy algorithm. The main thought is to divide the network into multiple clusters, merge them greedily and iteratively until the given number of controllers is satisfied, and place a controller in each cluster through the K-means algorithm. With the purpose of proving the effectiveness of GOKA, we conduct experiments to compare with Pareto Simulated Annealing (PSA), Adaptive Bacterial Foraging Optimization (ABFO), K-means and K-means[Formula: see text] on 6 real topologies from the Internet Topology Zoo and Internet2 OS3E. The results demonstrate that GOKA has a better and more stable solution than other four heuristic algorithms, and can decrease the propagation latency by up to [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] in contrast to PSA, ABFO, K-means and K-means[Formula: see text], respectively. Moreover, the error rate between GOKA and the best solution is always less than [Formula: see text], which promises the precision of our proposed algorithm.

Multi-objective optimization controller placement problem in internet-oriented software defined network

In Internet-oriented Software Defined Network (SDN), the feasibility and scalability are guaranteed by the cooperation among multiple controllers, thus the effective placement of these controllers becomes the significant task. In this paper, we formulate a Multi-objective Optimization Controller Placement (MOCP) problem, which focuses on achieving high network reliability, load balance among controllers, and low latency among controllers and switches. The MOCP covers three specific questions: where the controllers should be placed, how to assign controllers to switches, and how many routing requests should be processed by each controller. We use Adaptive Bacterial Foraging Optimization (ABFO) algorithm and redefine its computation rules to solve the MOCP problem. Simulation results demonstrate that the proposed ABFO based MOCP scheme is effective and has better performance than the existing ones.

Design optimization of CPW-fed microstrip patch antenna using constrained ABFO algorithm

This paper explores the potential of bio-inspired soft computational technique known as adaptive bacterial foraging optimization (ABFO) for the joint optimization of geometrical parameters of compact coplanar waveguide (CPW)-fed microstrip patch antenna with defected ground structure. The presented research work is divided into three phases. In the initial phase, the intended antenna is designed and analyzed using finite element-based electromagnetic simulator Ansoft HFSS 15.0. In the subsequent phase, the analytical equations of various design parameters are modeled using curve fitting technique in MATLAB and root mean square error-based fitness functions are derived for individual design parameters. Then, a joint cost function is formulated from individual fitness functions for evaluation in optimization algorithm. Adaptive BFO is an improvement in classical BFO algorithm that dynamically adjusts the run-length unit parameter to maintain the balance between exploration–exploitation trade-off. In the final phase, a variation in the adaptive BFO algorithm termed as ‘constrained ABFO’ is projected and designed to suit the bounded constraints imposed by anticipated antenna structure. The modified algorithm is efficaciously used for joint optimization of specific design parameters to transform ‘dual-band performance’ into ‘broadband performance’ for high-speed point-to-point wireless services. The performance of design optimization using constrained ABFO is compared with the original BFO, particle swarm optimization (PSO), hybrid bacterial foraging–particle swarm optimization (BF-PSO), invasive weed optimization (IWO) and artificial bee colony (ABC) techniques to scrutinize its adequacy.

Detection of Bundle Branch Block using Adaptive Bacterial Foraging Optimization and Neural Network

The medical practitioners analyze the electrical activity of the human heart so as to predict various ailments by studying the data collected from the Electrocardiogram (ECG). A Bundle Branch Block (BBB) is a type of heart disease which occurs when there is an obstruction along the pathway of an electrical impulse. This abnormality makes the heart beat irregular as there is an obstruction in the branches of heart, this results in pulses to travel slower than the usual. Our current study involved is to diagnose this heart problem using Adaptive Bacterial Foraging Optimization (ABFO) Algorithm. The Data collected from MIT/BIH arrhythmia BBB database applied to an ABFO Algorithm for obtaining best(important) feature from each ECG beat. These features later fed to Levenberg Marquardt Neural Network (LMNN) based classifier. The results show the proposed classification using ABFO is better than some recent algorithms reported in the literature.

Multi criteria simultaneous planning of passive filters and distributed generation simultaneously in distribution system considering nonlinear loads with adaptive bacterial foraging optimization approach

This paper presents adaptive bacterial foraging optimization (ABFO) algorithm to optimize the planning of passive power filters (PPFs) and distributed generations (DGs) in distribution system with presence of heavy nonlinear load simultaneously. The amount of nonlinear load is assumed to be serious which enforced the system planner to utilize the PPFS. Also the power loss minimization, reliability and voltage profile improvement, and other benefits encouraged the planner to employ the DGs.Some of DG technologies, such as wind generators and solar cells are based on power electronic devices and inverter implementation. Connection of this type of DGs to system produces harmonic. Therefore the utilization of PPFs among existent capacitor busses for harmonic compensation is inevitable. The objective is to minimize the power loss, the total harmonic distortion (THD) and the investment cost of PPFs and DGs simultaneously. Constraints include the voltage limits and the limit candidate buses for PPFs and DGs installation. The harmonic levels of system are obtained by current injections method and the load flow is solved by the iterative method of power sum, which is suitable for the accuracy requirements of this type of study. It is shown that through an economical planning of PPFs and DGs, the total voltage harmonic distortion and active power loss could be minimized simultaneously.

Bacterial foraging optimization and adaptive version for economically optimum sitting, sizing and harmonic tuning orders setting of LC harmonic passive power filters in radial distribution systems with linear and nonlinear loads

This paper presents bacterial foraging optimization (BFO) algorithm and its adaptive version to optimize the planning of passive harmonic filters (PHFs).The important problem of using PHFs is determining location, size and harmonic tuning orders of them, which is reach standard levels of harmonic distortion with applying minimum cost of passive filters.In this study to optimize the PHFs location, size and setting the harmonic tuning orders in the distribution system, considered objective function includes the reduction of power loss and investment cost of PHFs. At the same time, constraints include voltage limits, number/size of installed PHFs, limit candidate buses for PHFs installation and the voltage total harmonic distortion (THDv) in all buses. The harmonic levels of system are obtained by current injections method and the load flow is solved by the iterative method of power sum, which is suitable for the accuracy requirements of this type of study. It is shown that through an economical placement and sizing of PHFs the total voltage harmonic distortion and active power loss could be minimized simultaneously.The considered objective function is of highly non-convex manner, and also has several constraints. On the other hand due to significant computational time reduction and faster convergence of BFO in comparison with other intelligent optimization approach such as genetic algorithm (GA), particle swarm optimization (PSO) and artificial bee colony (ABC) the simple version of BFO has been implemented. Of course other versions of BFO such as Adaptive BFO and combination of BFO with other method due to complexity of harmonic optimization problem have not considered in this research.The simulation results for small scale test system with 10 buses, showed the significant computational time reduction and faster convergence of BFO in comparison with GA, PSO and ABC. Therefore in large scale radial system with 34 buses, the proposed method is solved using BFO.The simulation results for a 10-bus system as a small scale and 34-bus radial system as a large scale show that the proposed method is efficient for solving the presented problem.

Active noise control using an adaptive bacterial foraging optimization algorithm

This paper presents an adaptive bacterial foraging optimization (ABFO) algorithm for an active noise control system. The conventional active noise control (ANC) systems often use the gradient-based filtered-X least mean square algorithms to adapt the coefficients of the adaptive controller. Hence, there is a possibility to converge to local minima. In addition, this class of algorithms needs prior identification of the secondary path. The ABFO algorithm helps the ANC system to prevent falling into local minima. The proposed ANC system is also simpler since it does not need any prior information of the secondary path. Moreover, the adaptive strategy of the algorithm results in improved search performance compared with the basic bacterial foraging optimization algorithm, as well as other conventional algorithms. Experimental studies are performed for nonlinear primary path along with linear and nonlinear secondary path. The results show the effectiveness of the proposed ABFO-based ANC system for different kinds of input noise.

A comparison of swarm intelligence algorithms for structural engineering optimization

SUMMARYThis paper compares the performance of three swarm intelligence algorithms for the optimization of hard engineering problems. The algorithms tested were bacterial foraging optimization (BFO), particle swarm optimization (PSO), and artificial bee colony (ABC). Besides the regular BFO, two other variants reported in the literature were also included in the study: adaptive BFO and swarming BFO. Both PSO and ABC were tested using the regular algorithm and variants that include explosion (mass extinction). Three optimization problems of structural engineering were used: minimization of the cost of a welded beam, minimization of the construction cost of a pressure vessel, and minimization of the total weight of a 10‐bar plane truss. All problems are strongly constrained. The algorithms were evaluated using two criteria: quality of solutions and the number of function evaluations. The results show that PSO presented the best balance between these two criteria. For the optimization problems approached in this paper, we can also conclude that the explosion procedure resulted in no significant improvements. Copyright © 2012 John Wiley & Sons, Ltd.

Adaptive Bacterial Foraging Optimization

Bacterial Foraging Optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior ofE. colibacteria. Up to now, BFO has been applied successfully to some engineering problems due to its simplicity and ease of implementation. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques. This paper first analyzes how the run-length unit parameter of BFO controls the exploration of the whole search space and the exploitation of the promising areas. Then it presents a variation on the original BFO, called the adaptive bacterial foraging optimization (ABFO), employing the adaptive foraging strategies to improve the performance of the original BFO. This improvement is achieved by enabling the bacterial foraging algorithm to adjust the run-length unit parameter dynamically during algorithm execution in order to balance the exploration/exploitation tradeoff. The experiments compare the performance of two versions of ABFO with the original BFO, the standard particle swarm optimization (PSO) and a real-coded genetic algorithm (GA) on four widely-used benchmark functions. The proposed ABFO shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.

Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques

The present paper introduces the use of BFO and ABFO techniques to develop an efficient forecasting model for prediction of various stock indices. The structure used in these forecasting models is a simple linear combiner. The connecting weights of the adaptive linear combiner based models are optimized using ABFO and BFO by minimizing its mean square error (MSE). The short and long term prediction performance of these models are evaluated with test data and the results obtained are compared with those obtained from the genetic algorithm (GA) and particle swarm optimization (PSO) based models. It is in general observed that the new models are computationally more efficient, prediction wise more accurate and show faster convergence compared to other evolutionary computing models such as GA and PSO based models.