Bacterial Foraging Optimization Algorithm Research Articles

Hierarchical multilevel framework using RDWT-QR optimized watermarking in telemedicine

A hierarchical multilevel medical image watermarking framework is introduced in this paper. First, robust watermarking is performed to verify the copyright of the ownership. Later, an improved fragile watermarking is implemented on the robust watermarked image to achieve tamper localization along with self-recovery capability. The robust insertion is also optimized with the Particle Swarm Bacterial Foraging Optimization algorithm to ensure improved robustness and imperceptibility metrics of watermarking. During robust watermarking, multiple watermarks are concealed in the first column of upper triangular matrix ‘R,’ of the host image, transformed by a hybrid combination of Redundant Discrete Wavelet Transform–QR. This hybrid combination exploits the redundancy in Redundant Discrete Wavelet Transform to improve the payload capacity, imperceptibility and the low computational QR transform is used to make watermarking scheme free from the false positive problem. First, the cover image is fragmented into blocks of 2 × 2 dimension, and then, on each block, QR decomposition is carried out. The upper triangular matrix pattern is utilized to figure out the tamper localization bits for each block. Fragile watermarking is implemented on the robust watermarked image, in which the watermark is formed by encoding all 2 × 2 blocks with QR coefficients which are hidden into blocks reserved for self recovery. Authentication of watermark bits, which is different for each watermarked image, helps in resisting the block-based attacks such as Vector Quantization, Collage and Constant Average Attacks. In comparison with the existing works in literature, the proposed framework offers high average Peak Signal-to-Noise Ratio and Normalized Cross Correlation in the robust watermarking. In addition to this, excellent results is achieved in the fragile watermarking with an overall tamper detection rate of 99.7% and an overall recovery of 57 dB Peak Signal-to-Noise Ratio for a 50% tampering.

Recognition Using DNN with Bacterial Foraging Optimization Using MFCC Coefficients

Speech is an important mode of communication for people. For a long time, researchers have been working hard to develop conversational machines which will communicate with speech technology. Voice recognition is a part of a science called signal processing. Speech recognition is becoming more successful for providing user authentication. The process of user recognition is becoming more popular now a days for providing security by authenticating the users. With the rising importance of automated information processing and telecommunications, the usefulness of recognizing an individual from the features of user voice is increasing. In this paper, the three stages of speech recognition processing are defined as pre-processing, feature extraction and decoding. Speech comprehension has been significantly enhanced by using foreign languages. Automatic Speech Recognition (ASR) aims to translate text to speech. Speaker recognition is the method of recognizing an individual through his/her voice signals. The new speaker initially privileges identity for speaker authentication, and then the stated model is used for identification. The identity argument is approved when the match is above a predefined threshold. The speech used for these tasks may be either text-dependent or text-independent. The article uses Bacterial Foraging Optimization Algorithm (BFO) for accurate speech recognition through Mel Frequency Cepstral Coefficients (MFCC) model using DNN. Speech recognition efficiency is compared to that of the conventional system.

A multi-subpopulation bacterial foraging optimisation algorithm with deletion and immigration strategies for unmanned surface vehicle path planning

With the advantages of flexible control ability, unmanned surface vehicle (USV) has been widely applied in civil and military fields. A number of researchers have been working on the development of intelligent path planning algorithms to plan a high-quality and collision-free path which is applied to guide USV through cluttered environments. The conventional algorithms may either have issues with trapping into a local optimal solution or face a slow convergence problem. This paper presents a novel multi-subpopulation bacterial foraging optimisation (MS-BFO) algorithm for USV path planning that enhances the searching performance, especially, in a complex environment. This method constructs the deletion and immigration strategies (DIS), which guarantees the elite optimised individual of each subpopulation to be inherited by others, thus to consequently lead to fast convergence speed. The experimental results show that the proposed method is able to suggest an optimised path within the shortest length of time, compared with other optimisation algorithms.

Evaluation of compacted lateritic soil – bacillus coagulans induced calcite precipitate for landfill application using bacterial foraging optimization algorithm

Evaluation of compacted lateritic soil – Bacillus coagulans (B.coagulans) induced calcite precipitate for landfill application was carried out using Bacterial Foraging Optimization Algorithm (BFOA). Experimental results were obtained through laboratory tests. Measured soil factors include; Coefficient of permeability (hydraulic conductivity) as a dependent factor and void ratio, bulk density, B. coagulans suspension, pH, Compactive effort, water content relative to optimum, plasticity index, viscosity of microbes and liquid limit as self-determining (independent) factors. GeneXproTools 5.0 software was used to develop the fitness (objective) function used for Bacterial Foraging Optimization (BFO) to predict hydraulic conductivity of treated lateritic soil with B. coagulans suspension density. The fitness function was then integrated using BFO codes in Matlab 2016 version to predict the minimum hydraulic conductivity for a given set of self-determining factors. The result shows a strong association between the predicted hydraulic conductivity values and the measured hydraulic conductivity values in the laboratory using a regressed line with coefficient of determination R=0.85. It is clear from the optimization results that the hydraulic conductivity values and the self-determining variables have strong correlation. The least hydraulic conductivity value of 2.38×10−11m/s which falls below the permissible regulatory value of 1.0×10−9 m/s for landfill application was achieved at 100 iterations. Based on all the optimization results, it is recommended that lateritic soil-B.coagulans mixtures be compacted with 1.20×10−9 - 2.40×10−9 cells/ml B.coagulans suspension density compacted using Britsh standard heavy (BSH) energy at 15.3 % moisture content for landfill application.

Supplier side optimal bidding strategy for electricity market using bacterial foraging optimization algorithm

<p class="Title1">In this article, bacterial foraging optimization (BFO) algorithm is developed for single side optimal bidding strategy in an electricity market. Optimal bidding strategy is one of the important functions in the electricity market along with forecasting of the electricity price and the profit based unit commitment. The prime objective of generating company (Genco) is to maximize their profit when they participate in the bidding process. The BFO algorithm has been used to maximize the probability density function (pdf). In the second stage the BFO algorithm is again applied to maximize the profit of the suppliers. The Proposed algorithm is developed in MATLAB (Version, 2019) and tested on standard test case available in the literature. Also, the simulation results are presented and compared. It is noticed that the proposed method yields the best results in terms of profit.</p>

A Collision Avoidance Method for Intelligent Ship Based on the Improved Bacterial Foraging Optimization Algorithm

The bacterial foraging optimization algorithm (BFOA) is an intelligent population optimization algorithm widely used in collision avoidance problems; however, the BFOA is inappropriate for the intelligent ship collision avoidance planning with high safety requirements because BFOA converges slowly, optimizes inaccurately, and has low stability. To fix the above shortcomings of BFOA, an autonomous collision avoidance algorithm based on the improved bacterial foraging optimization algorithm (IBFOA) is demonstrated in this paper. An adaptive diminishing fractal dimension chemotactic step length is designed to replace the fixed step length to achieve the adaptive step length adjustment, an optimal swimming search method is proposed to solve the invalid searching and repeated searching problems of the traditional BFOA, and the adaptive migration probability is developed to take the place of the fixed migration probability to prevent elite individuals from being lost in BOFA. The simulation of benchmark tests shows that the IBFOA has a better convergence speed, optimized accuracy, and higher stability; according to a collision avoidance simulation of intelligent ships which applies the IBFOA, it can realize the autonomous collision avoidance of intelligent ships in dynamic obstacles environment is quick and safe. This research can also be used for intelligent collision avoidance of automatic driving ships.

A Bacterial Chemotaxis-Inspired Coordination Strategy for Coverage and Aggregation of Swarm Robots

Many bio-inspired coordination strategies have been investigated for swarm robots. Bacterial chemotaxis exhibits a certain degree of intelligence, and has been developed some optimization algorithm for robot(s), e.g., bacterial foraging optimization algorithm (BFOA) and bacterial colony chemotaxis optimization algorithm (BCC). This paper proposes a bacterial chemotaxis-inspired coordination strategy (BCCS) of swarm robotic systems for coverage and aggregation. The coverage is the problem of finding a solution to uniformly deploy robots on a given bounded space. To solve this problem, this paper uses chaotic preprocessing to generate the initial positions of the robots. After the initialization, each robot calculates the area only covered by itself as the fitness function value. Then, each robot makes an action, running or rotating, depending on coordination strategy inspired bacterial chemotaxis. Moreover, we extend this solution and introduce a random factor to overcome aggregation, which is to guide robots to rendezvous at an unspecified point. The simulation results demonstrate the superior performance of the proposed coordination strategy in both success rate and an average number of iterations than other controllers.

A multi-modal bacterial foraging optimization algorithm

In recent years, multi-modal optimization algorithms have attracted considerable attention, largely because many real-world problems have more than one solution. Multi-modal optimization algorithms are able to find multiple local/global optima (solutions), while unimodal optimization algorithms only find a single global optimum (solution) among the set of the solutions. Niche-based multi-modal optimization approaches have been widely used for solving multi-modal problems. These methods require a predefined niching parameter but estimating the proper value of the niching parameter is challenging without having prior knowledge of the problem space. In this paper, a novel multi-modal optimization algorithm is proposed by extending the unimodal bacterial foraging optimization algorithm. The proposed multi-odal bacterial foraging optimization (MBFO) scheme does not require any additional parameter, including the niching parameter, to be determined in advance. Furthermore, the complexity of this new algorithm is less than its unimodal form because the elimination-dispersal step is excluded, as is any other phase, like a clustering or local search algorithm. The algorithm is compared with six multi-modal optimization algorithms on nine commonly used multi-modal benchmark functions. The experimental results demonstrate that the MBFO algorithm is useful in solving multi-modal optimization problems and outperforms other methods.

A survey of bacterial foraging optimization

Bacterial foraging optimization algorithm (BFO) is a biological-inspired swarm intelligence optimization algorithm that simulates the foraging behavior of bacteria to obtain maximal energy during the searching process. Since its inception, it has evoked wide attention from researchers. The number of articles about BFO and its variants has grown significantly over the past decades and continues to grow. Hence, there is a clear need for a scientific and comparative review of extant BFO literature based on a common framework that offers a quick glimpse of the development of BFO. In light of this, we develop a bibliometric review via using CiteSpace to construct a knowledge mapping, and then conduct a thorough literature review of BFO research. Based on the analytical results, we present the influential researchers, journals or proceedings, and articles, and discuss various key issues of BFO. Finally, we suggest serval research directions that require further attention. This survey is expected to exhibit a lucid outline and useful guidance for the researchers of BFO.

An Efficient Genetic Hybrid PAPR Technique for 5G Waveforms

Non-orthogonal multiple access (NOMA) is a strong contender multicarrier waveform technique for the fifth generation (5G) communication system. The high peak-to-average power ratio (PAPR) is a serious concern in designing the NOMA waveform. However, the arrangement of NOMA is different from the orthogonal frequency division multiplexing. Thus, traditional reduction methods cannot be applied to NOMA. A partial transmission sequence (PTS) is commonly utilized to minimize the PAPR of the transmitting NOMA symbol. The choice phase aspect in the PTS is the only non-linear optimization obstacle that creates a huge computational complication due to the respective non-carrying sub-blocks in the unitary NOMA symbol. In this study, an efficient phase factor is proposed by presenting a novel bacterial foraging optimization algorithm (BFOA) for PTS (BFOA-PTS). The PAPR minimization is accomplished in a two-stage process. In the initial stage, PTS is applied to the NOMA signal, resulting in the partition of the NOMA signal into an act of sub-blocks. In the second stage, the best phase factor is generated using BFOA. The performance of the proposed BFOA-PTS is thoroughly investigated and compared to the traditional PTS. The simulation outcomes reveal that the BFOA-PTS efficiently optimizes the PAPR performance with inconsequential complexity. The proposed method can significantly offer a gain of 4.1 dB and low complexity compared with the traditional OFDM.

Implementation of Optimization-Based PI Controller Tuning for Non-Ideal Differential Boost Inverter

The demand for renewable energy to sustain today’s vulnerability towards depleting fossil fuels is a crucial agenda for research. Various inverter topologies have been proposed to convert renewable sources into a usable form. But output THD, additional filtering components at line frequency (leading to bulky circuitry), lower efficiency, etc., are some of the limitations faced in all those topologies. This paper aims to change a voltage source inverter’s traditional behavior, which generates lesser output voltage with higher THD. The paper proposes a closed-loop non-ideal differential boost inverter (DBI) employing a PI controller. The optimization techniques such as, genetic algorithm (GA) and bacterial foraging optimization algorithm (BFOA) are incorporated to accentuate the PI controller’s performance to produce a better response during line and load disturbance conditions with reduced THD. DBI performance is evaluated on a laboratory prototype with different loading conditions. A comparison between the algorithms and the previous topologies from the literature survey has also been provided to validate this research’s claims. This paper’s required simulation study is carried out using MATLAB, and real-time validation is carried out using dSPACE 1104 with sampling time of one $\mu \text{s}$ .

Optimization of cold chain distribution path of fresh agricultural products under carbon tax mechanism: A case study in China

According to the problem of large amount of carbon emissions during the cold chain distribution process, a cold chain distribution route optimization method for fresh agricultural products under the carbon tax mechanism was proposed. Firstly, with the goal of minimizing carbon emission cost and comprehensive cost, quantitative analysis of carbon tax mechanism is introduced, considering the demand quantity, demand time and unloading time constraints, a mathematical model of the problem is established. In addition, an improved quantum bacterial foraging optimization algorithm is put forward, which uses the bacterial optimization algorithm information update strategy to maintain group memory, and uses the carbon tax cost as the decision variable of the improved algorithm. Through experimental simulation, comparative analysis of the shortest distribution path, uninitialized pheromone bacterial foraging optimization algorithm and quantum bacterial foraging optimization algorithm on the last selected study model, the method proposed in this thesis can effectively optimize the distribution path, reduce carbon tax cost and comprehensive cost.

WITHDRAWN: Bacterial foraging optimization algorithm based feature selection for microarray data classification

An effective method is needed to extract knowledge and useful information from microarray gene expression datasets. Datasets of microarray gene expression typically consist of large numbers of genes and less samples, making it very difficult to extract data and trends present in the data. To choose the genes with maximum variations that are the most informative and important genes, gene selection is therefore carried out. The genes whose value in the samples does not differ much and remain roughly the same are excluded and are not used for the classifier design. Recent research has focused on the use of data mining to classify microarray data and to identify genes that are differentially regulated by different diseases. In this paper, gene selection was based on the technique of bacterial foraging optimization (BFO). E.coli bacteria develop at a very rapid rate due to the suitable conditions and adequate food, founded on the drinking performance of the Bacteria E.coli. The bacteria are moving into nutrient areas very rapidly and seeking to escape harmful substances. The bacterial movements are called taxes.

Optimal Placement of Electric Vehicle Charging Stations in a Distribution Network With Randomly Distributed Rooftop Photovoltaic Systems

The increasing number of electric vehicles (EVs) in today’s transport sector is gradually leading to the phasing out of petroleum-based vehicles. However, the rapid deployment of EVs largely depends on the coordinated and fast expansion of EV charging stations (EVCSs). The integration of EVCSs in the modern distribution network characterized by increased penetration of randomly distributed photovoltaic (PV) systems is challenging as they can lead to excessive power losses and voltage deviations beyond acceptable limits. In this paper, a hybrid bacterial foraging optimization algorithm and particle swarm optimization (BFOA-PSO) technique is proposed for the optimal placement of EVCSs into the distribution network with high penetration of randomly distributed rooftop PV systems. The optimization problem is formulated as a multi-objective problem minimizing active and reactive power losses, average voltage deviation index, and maximizing voltage stability index. The IEEE 69 node distribution network is used as the case network. The simulation is done using MATLAB to integrate the EVCSs in five cases of randomly sized and placed PV systems in the distribution network. For all five cases, a minimal increase in power losses is recorded with minor changes in the voltage deviation and stability indices due to the placement of the EVCSs. But for the voltages of nodes 29 to 48, the other node voltages remain unchanged upon placement of the EVCSs. The largest increase in power losses due to the EVCSs being brought into the network with PVs was noticed in case 3 (from 142.27kW, and 62.90kVar to 147.65kW, and 72.48kVar).

Diminution of factual power loss by enhanced bacterial foraging optimization algorithm

<div data-canvas-width="126.37004132231402">This paper presents an enhanced bacterial foraging optimization (EBFO) algorithm for solving the optimal reactive power problem. Bacterial foraging optimization is based on foraging behaviour of <em>Escherichia coli</em> bacteria which present in the human intestine. Bacteria have inclination to congregate the nutrient-rich areas by an action called as Chemo taxis. The bacterial foraging process consists of four chronological methods i.e. chemo taxis, swarming and reproduction and elimination-dispersal. In this work rotation angle adaptively and incessantly modernized, which augment the diversity of the population and progress the global search capability. The quantum rotation gate is utilized for chemo taxis to modernize the state of chromosome projected EBFO algorithm has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.</div>

Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm

The bacterial foraging optimization (BFO) algorithm successfully searches for an optimal path from start to finish in the presence of obstacles over a flat surface map. However, the algorithm suffers from getting stuck in the local minima whenever non-circular obstacles are encountered. The retrieval from the local minima is crucial, as otherwise, it can cause the failure of the whole task. This research proposes an improved version of BFO called robust bacterial foraging (RBF), which can effectively avoid obstacles, both of circular and non-circular shape, without falling into the local minima. The virtual obstacles are generated in the local minima, causing the robot to retract and regenerate a safe path. The proposed method is easily extendable to multiple robots that can coordinate with each other. The information related to the virtual obstacles is shared with the whole swarm, so that they can escape the same local minima to save time and energy. To test the effectiveness of the proposed algorithm, a comparison is made against the existing BFO algorithm. Through the results, it was witnessed that the proposed approach successfully recovered from the local minima, whereas the BFO got stuck.

Coordinated photovoltaic re-phasing: A novel method to maximize renewable energy integration in low voltage networks by mitigating network unbalances

As combating climate change has become a top priority and as many countries are taking steps to make their power generation sustainable, there is a marked increase in the use of renewable energy sources (RESs) for electricity generation. Among these RESs, solar photovoltaics (PV) is one of the most popular sources of energy connected to LV distribution networks. With the greater integration of solar PV into LV distribution networks, utility providers impose caps to solar penetration in order to operate their network safely and within acceptable norms. One parameter that restricts solar PV penetration is unbalances created by loads and single-phase rooftop schemes connected to LV distribution grids. In this paper, a novel method is proposed to mitigate voltage unbalance in LV distribution grids by optimally re-phasing grid-connected rooftop PV systems. A modified version of the discrete bacterial foraging optimization algorithm (DBFOA) is introduced as the optimization technique to minimize the overall voltage unbalance of the network as the objective function, subjected to various network and operating parameters. The impact of utilizing the proposed PV re-phasing technique as opposed to a fixed phase configuration are compared based on overall voltage unbalance, which was observed hourly throughout the day. The case studies show that the proposed approach can significantly mitigate the overall voltage unbalance during the daytime and can facilitate to increase the usable PV capacity of the considered network by 77%.

Implementation of PWM AC chopper controller for capacitor run induction motor drive via bacterial foraging optimization algorithm

<p>This paper focuses on design of closed-loop control for pulse width modulated AC chopper controlled capacitor run induction motor drive engaging enriched optimization algorithm based on foraging of bacteria. Capacitor run induction motor is a non-linear device and its parameter varies under different functional point of the system. A linearized increment model for PWM AC chopper is illustrated for a particular functional point of the drive. The conventional method does not provide acceptable performance under different load conditions. Bacteria foraging optimization technique categorizes accurate control parameters for the superlative dynamic response under unit step load variations. Field Programmable Gate Array is implemented practically for a particular functional point of the drive to exhibit accurate performance. Experimental and simulated results are obtained to authenticate the effectiveness of the optimized controller.</p><p> </p>

Analysis influence factors and forecast energy-related CO2 emissions: evidence from Hebei.

With economic development and the acceleration of urbanization, China's energy demand has gradually increased and brought a lot of energy-related CO2 emissions. Energy-related CO2 emissions are affected by a variety of factors. Quantifying the correlation between energy-related CO2 and driving factors and constructing the driving factor system are conducive to predict the future energy-related CO2 emissions and analyze the impact of driving factors. In this paper, the improved grey relational analysis (IGRA) was proposed to screen the influencing factors of energy-related CO2 emissions considering the sample difference, and the factor analysis (FA) was used to reduce dimensionality of the influencing factors. Then, a carbon dioxide emission forecasting model based on the bacterial foraging optimization algorithm (BFO) and the least square support vector machine (LSSVM) was proposed. Empirical analysis results of Hebei show that the LSSVM optimized BFO significantly improves the accuracy of energy-related CO2 emissions forecasting, and IGRA-FA-BFOLSSVM model is significantly better than BP, PSOBP, SVM, and LSSVM models. The mean absolute percentage error (MAPE) of the proposed model is 0.374%. The forecasting results of the supplementary case show that the model has better generalization ability. In addition, education and technological progress have proven to be important drivers of energy-related CO2 emissions. Simultaneously, the research results can also offer more breakthrough points for policy makers to control carbon emissions.

Optimization learning of hidden Markov model using the bacterial foraging optimization algorithm for speech recognition

Nowadays, the speech recognition applications can be found in several activities, and their existence as a field of study and research lasts for a long time. Although, many studies deal with different problems, in security-related areas, biometric identification, access to the Smartphone… Etc. In automatic speech recognition (ASR) systems, hidden Markov models (HMMs) have widely used for modeling the temporal speech signal. In order to optimize HMM parameters (i.e., observation and transition probabilities), iterative algorithms commonly used such as Forward-Backward or Baum-Welch. In this article, we propose to use the bacterial foraging optimization algorithm (BFOA) to enhance HMM with Gaussian mixture densities. As a global optimization algorithm of current interest, BFOA has proven itself for distributed optimization and control. Our experimental results show that the proposed approach yields a significant improvement of the transcription accuracy at signal/noise ratios greater than 15 dB.