Bacterial Foraging Optimization Research Articles

Optimizing Extreme Learning Machine for Drought Forecasting: Water Cycle vs. Bacterial Foraging

Machine learning (ML) methods have shown noteworthy skill in recognizing environmental patterns. However, presence of weather noise associated with the chaotic characteristics of water cycle components restricts the capability of standalone ML models in the modeling of extreme climate events such as droughts. To tackle the problem, this article suggests two novel hybrid ML models based on combination of extreme learning machine (ELM) with water cycle algorithm (WCA) and bacterial foraging optimization (BFO). The new models, respectively called ELM-WCA and ELM-BFO, were applied to forecast standardized precipitation evapotranspiration index (SPEI) at Beypazari and Nallihan meteorological stations in Ankara province (Turkey). The performance of the proposed models was compared with those the standalone ELM considering root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and graphical plots. The forecasting results for three- and six-month accumulation periods showed that the ELM-WCA is superior to its counterparts. The NSE results of the SPEI-3 forecasting in the testing period proved that the ELM-WCA improved drought modeling accuracy of the standalone ELM up to 72% and 85% at Beypazari and Nallihan stations, respectively. Regarding the SPEI-6 forecasting results, the ELM-WCA achieved the highest RMSE reduction percentage about 63% and 56% at Beypazari and Nallihan stations, respectively.

Digital Image Watermarking with Hybrid Structure of DWT, DCT, SVD Techniques and The Optimization with BFO Algorithm

The copyright violations in digital images and the violations of the privacy of personal data are happened with the development of technology and the widespread use of the internet. The usage of watermarks in digital images provides high protection to image owners in copyright protection and in protection of personal data. In this paper, watermarks have been added to digital images by using discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD) methods, respectively. “Gaussian”, “shot”, “salt & pepper” and “speckle” noises are added to watermarked images, respectively. The original image and the watermark have been extracted from the watermarked image after adding noise. The original image and watermark have been re-extracted from the optimized watermarked image by using the bacterial foraging optimization (BFO) technique. In this step, the particle swarm optimization (PSO) algorithm has been used to set the best position of the chemotactic parameters of the BFO algorithm. The peak signal to noise ratio (PSNR), normalized cross correlation (NCC) and image fidelity (IF) values have been calculated and compared to see the success performance of watermarking techniques and optimization techniques.

Intelligent Deep Learning Enabled Wild Forest Fire Detection System

The latest advancements in computer vision and deep learning (DL) techniques pave the way to design novel tools for the detection and monitoring of forest fires. In this view, this paper presents an intelligent wild forest fire detection and alarming system using deep learning (IWFFDA-DL) model. The proposed IWFFDA-DL technique aims to identify forest fires at earlier stages through integrated sensors. The proposed IWFFDA-DL system includes an Integrated sensor system (ISS) combining an array of sensors that acts as the major input source that helps to forecast the fire. Then, the attention based convolution neural network with bidirectional long short term memory (ACNN-BLSTM) model is applied to examine and identify the existence of danger. For hyperparameter tuning of the ACNN-BLSTM model, the bacterial foraging optimization (BFO) algorithm is employed and thereby enhances the detection performance. Finally, when the fire is detected, the Global System for Mobiles (GSM) modem transmits messages to the authorities to take required actions. An extensive set of simulations were performed and the results are investigated interms of several aspects. The obtained results highlight the betterment of the IWFFDA-DL technique interms of various measures.

Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification

Autism spectrum disorder (ASD) is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills, recurrent conduct, and communication. Identifying ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with ASD. Recognition of ASD related to objective pathogenic mutation screening is the initial step against prior intervention and efficient treatment of children who were affected. Nowadays, healthcare and machine learning (ML) industries are combined for determining the existence of various diseases. This article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification (JSODL-ASDDC) model. The goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical data. The proposed JSODL-ASDDC model initially performs min-max data normalization approach to scale the data into uniform range. In addition, the JSODL-ASDDC model involves JSO based feature selection (JFSO-FS) process to choose optimal feature subsets. Moreover, Gated Recurrent Unit (GRU) based classification model is utilized for the recognition and classification of ASD. Furthermore, the Bacterial Foraging Optimization (BFO) assisted parameter tuning process gets executed to enhance the efficacy of the GRU system. The experimental assessment of the JSODL-ASDDC model is investigated against distinct datasets. The experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches.

MBAHIL: Design of a Multimodal Hybrid Bioinspired Model for Augmentation of Hyperspectral Imagery via Iterative Learning for Continuous Efficiency Enhancements

The augmentation of hyperspectral images requires the design of high-density feature analysis & band-fusion models that can generate multimodal imagery from limited information sets. The feature analysis models use deep learning operations to maximize inter-class variance while minimizing inter-class variance levels for efficient classification operations. When combined with intelligent band-fusion methods, such models allow the augmentation model to enhance its classification efficiency under different use cases. Existing band-fusion-based augmentation models for hyperspectral images do not incorporate continuous efficiency enhancements and showcase higher complexity levels. Furthermore, these models can’t be scaled for more varied use cases because their use is restricted to specific image types. To overcome these issues, we designed a novel multimodal hybrid bioinspired model for the augmentation of hyperspectral imagery via iterative learning for continuous efficiency enhancements. The proposed model initially represents input images into Fourier, Laplacian, Cosine, multimodal Wavelet, Mellin, and Z-Transform domains, which will assist in describing the images in multimodal domains. These transformed image sets are passed through a convolutional filter to extract windowed feature sets. A Grey Wolf Optimizer (GWO) is used to identify high inter-class variance features from the extracted image sets, which assists in selecting transformed images that can help improve hyperspectral classification performance. The selected hyperspectral images are fused via a Bacterial Foraging Optimization (BFO) model, which assists in reducing intra-class variance levels. The final set of selected images is intelligently augmented via Particle Swarm Optimization (PSO), which performs rotation, zooming, shifting, and brightness variation operations selectively. These augmented images are classified via a customized VGGNet-19-based Convolutional Neural Network (CNN) classifier that assists in continuously estimating accuracy levels for different application scenarios. Based on these accuracy levels, the model is reconfigured via hyperparameter tuning of GWO and PSO optimizers. Due to combining these models and incremental accuracy optimizations, the proposed model has improved its hyperspectral classification accuracy by 10.6% and precision by 10.4%, as compared to standard deep learning-based augmentation techniques.

Artificial Intelligence Enabled Decision Support System on E-Healthcare Environment

In today’s digital era, e-healthcare systems exploit digital technologies and telecommunication devices such as mobile devices, computers and the internet to provide high-quality healthcare services. E-healthcare decision support systems have been developed to optimize the healthcare services and enhance a patient’s health. These systems enable rapid access to the specialized healthcare services via reliable information, retrieved from the cases or the patient histories. This phenomenon reduces the time taken by the patients to physically visit the healthcare institutions. In the current research work, a new Shuffled Frog Leap Optimizer with Deep Learning-based Decision Support System (SFLODL-DSS) is designed for the diagnosis of the Cardiovascular Diseases (CVD). The aim of the proposed model is to identify and classify the cardiovascular diseases. The proposed SFLODL-DSS technique primarily incorporates the SFLO-based Feature Selection (SFLO-FS) approach for feature subset election. For the purpose of classification, the Autoencoder with Gated Recurrent Unit (AEGRU) model is exploited. Finally, the Bacterial Foraging Optimization (BFO) algorithm is employed to fine-tune the hyperparameters involved in the AEGRU method. To demonstrate the enhanced performance of the proposed SFLODL-DSS technique, a series of simulations was conducted. The simulation outcomes established the superiority of the proposed SFLODL-DSS technique as it achieved the highest accuracy of 98.36%. Thus, the proposed SFLODL-DSS technique can be exploited as a proficient tool in the future for the detection and classification of CVD.

A modified bacterial foraging algorithm for improving road networks

One of the challenges that transportation planners face is deciding which improvements to make to existing road networks. A solution methodology for this problem must consider multiple factors, such as motorist travel behaviour and budget constraints. This is commonly represented as a bi-level optimization problem called the Discrete Network Design Problem (DNDP). In this paper, we devise and implement a Modified Bacterial Foraging Optimization (MBFO) algorithm that provides the decision maker with n-optimal solutions to the DNDP. This enables transportation planners to evaluate and compare various improvements before deciding which ones to implement. The use of a binary mapping function facilitates the implementation of Bacterial Foraging Optimization (BFO) to solve the DNDP. The MBFO employs a modified chemotaxis step with a single mutation operation to represent the tumbling motion and a revised swimming operation to ensure a diverse search of the solution space. To rank and eliminate the solutions, a gain index that measures improvement in fitness function value is used. The MBFO is tested on the 24-node, 76-arc Sioux Falls network, and the results show that it can converge to the n-optimal solution.

Daily streamflow forecasting by machine learning in Tra Khuc river in Vietnam

Precise streamflow prediction is crucial in the optimization of the distribution of water resources. This study develops the machine learning models by integrating recurrent gate unit (GRU) with bacterial foraging optimization (BFO), gray wolf optimizer (GWO), and human group optimization (HGO) to forecast the streamflow in the Tra Khuc River, Vietnam. For this purpose, the time series of daily rainfall and river flow at Son Giang station from 2000 to 2020 were employed to forecast the streamflow. The statistical indices, namely the root mean square error, the mean absolute error, and the coefficient of determination (R²), was utilized to evaluate the performance of the proposed models. The results showed that the three optimization algorithms (HGO, GWO, and BFO) effectively enhanced the performance of the GRU model.
 Moreover, among the four models (GRU, GRU-HGO, GRU-GWO, and GRU-BFO), the GRU-GWO model outperformed the other models with R² = 0.883. GRU-HGO achieved R² = 0.879, and GRU-BFO achieved R²=0.878. The results of this study showed that GRU combined with optimization algorithms is a reliable modeling approach in short-term flow forecasting.

Multilevel thresholding for crop image segmentation based on recursive minimum cross entropy using a swarm-based technique

The segmentation of crop images is the most sought-after technology in the field of computer vision and pattern recognition. The multilevel thresholding (MLT) of crop images is a tedious job due to the complex background and uneven distribution of intensity levels. The recursive minimum cross entropy (R-MCE) approach is a notable technique for MLT. Therefore, in this paper, R-MCE has been used with an efficient cuckoo search algorithm (CSA), based on Lévy flight for determining the best thresholds. This technique reduces the complexity of computation and increases the accuracy, when it is used for MLT. The dexterity of the proposed technique has been examined over 20 crop images with different illumination and complex backgrounds. Results of the proposed method have been expressed in terms of feature similarity index (FSIM), structural similarity index (SSIM), mean square error (MSE), peak signal-to-noise ratio (PSNR), and CPU time. To investigate the efficiency of proposed technique, the segmented results have been compared with well-known thresholding methods, such as wind-driven optimization (WDO), bacterial foraging optimization (BFO), beta differential evolution (BDE), and artificial bee colony (ABC). Simulation results show that the MLT of the proposed approach can accurately and efficiently segment the complex background crop images.

An Enhanced Approach for Image Edge Detection Using Histogram Equalization (BBHE) and Bacterial Foraging Optimization (BFO)

An Enhanced Approach for Image Edge Detection Using Histogram Equalization (BBHE) and Bacterial Foraging Optimization (BFO)

An improved segmentation technique for multilevel thresholding of crop image using cuckoo search algorithm based on recursive minimum cross entropy

Crop image segmentation is widely used for the analysis of crops. A wide variety of crops are present in the agriculture field, which varies in intensity and complex backgrounds. The thresholding method based on entropy is quite popular for the segmentation of an image. Among all, minimum cross entropy has been widely used. However, the complexity of computation increases when it is used for multilevel thresholding (MLT). Recursive minimum cross entropy is used to resolve the complexity of computation, and cuckoo search (CS) using Levy flight is used to find the optimal threshold for this objective function. Because real-time applications require less processing time while maintaining high performance, which is validated by the CS algorithm using recursive minimum cross entropy (R-MCE-CS) without constraint. The proposed method uses one constraint based on the structural similarity index (SSIM), which leads to an increment in the accuracy for a higher level of thresholding. The accuracy of the proposed method has been tested over 10 crop images with complex backgrounds and high dimensions of colour intensity space. The outcome of the proposed technique has been compared with five algorithms such as wind-driven optimisation (WDO), bacterial foraging optimisation (BFO), differential evolution (DE), artificial bee colony (ABC), and firefly algorithm (FFA). The result shows that the proposed method gives the most promising result, and the accuracy is also improved.

A multi-resolution grid-based bacterial foraging optimization algorithm for multi-objective optimization problems

In recent years, bacterial foraging optimization (BFO) has been used to solve multiobjective optimization problems (MOPs). However, BFO has not fully developed its potentials on MOPs for the reason of lacking of in-depth research on the optimization mechanisms and the diversity maintenance strategies. To solve it, this paper develops a multi-resolution grid-based BFO algorithm (called as MRBFO). MRBFO redesigns four tailored optimization mechanisms for MOPs including chemotaxis, conjugation, reproduction, and elimination and dispersal to search optimal nondominated solutions. Moreover, MRBFO defines a multi-resolution grid strategy to produce well-distributed diverse nondominated solutions. The performance of MRBFO is comprehensively evaluated by comparing it with several state-of-the-art algorithms on many benchmark test problems. The empirical results have sufficiently verified the advantages of MRBFO.

Class Imbalance Handling with Deep Learning Enabled IoT Healthcare Diagnosis Model

The rapid advancements in the field of big data, wearables, Internet of Things (IoT), connected devices, and cloud environment find useful to improve the quality of healthcare services. Medical data classification using the data collected by the wearables and IoT devices can be used to determine the presence or absence of disease. The recently developed deep learning (DL) models can be used for several processes such as classification, natural language processing, etc. This study presents a bacterial foraging optimization (BFO) based convolutional neural network-gated recurrent unit (CNN-GRU) with class imbalance handling (CIH) model, named BFO-CNN-GRU-CIH for medical data classification in IoT enabled cloud environment. The proposed BFO-CNN-GRU-CIH model initially enables the IoT devices to gather healthcare data and preprocess it for further processing. In addition, Lempel Ziv Markov chain Algorithm (LZMA) is employed for the compression of healthcare data to reduce the amount of data being communicated. Besides, Synthetic Minority Over-sampling Technique (SMOTE) is applied to handle class imbalance data problems. Moreover, BFO with CNN-GRU model is utilized to perform the classification process in which the hyperparameters of the CNN-GRU model are optimally adjusted by the use of BFO algorithm. In order to showcase the better performance of the BFO-CNN-GRU-CIH model, a wide range of simulations take place on three benchmark datasets and the results portrayed the betterment of the BFO-CNN-GRU-CIH model over the recent state of art approaches.

Ensemble of Handcrafted and Deep Learning Model for Histopathological Image Classification

Histopathology is the investigation of tissues to identify the symptom of abnormality. The histopathological procedure comprises gathering samples of cells/tissues, setting them on the microscopic slides, and staining them. The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge. At the same time, deep learning (DL) techniques are able to derive features, extract data, and learn advanced abstract data representation. With this view, this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification (EHCDL-HIC) model. The proposed EHCDL-HIC technique initially performs Weiner filtering based noise removal technique. Once the images get smoothened, an ensemble of deep features and local binary pattern (LBP) features are extracted. For the classification process, the bidirectional gated recurrent unit (BGRU) model can be employed. At the final stage, the bacterial foraging optimization (BFO) algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance, shows the novelty of the work. For validating the enhanced execution of the proposed EHCDL-HIC method, a set of simulations is performed. The experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%. Therefore, the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.

Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis

The abundant existence of both structured and unstructured data and rapid advancement of statistical models stressed the importance of introducing Explainable Artificial Intelligence (XAI), a process that explains how prediction is done in AI models. Biomedical mental disorder, i.e., Autism Spectral Disorder (ASD) needs to be identified and classified at early stage itself in order to reduce health crisis. With this background, the current paper presents XAI-based ASD diagnosis (XAI-ASD) model to detect and classify ASD precisely. The proposed XAI-ASD technique involves the design of Bacterial Foraging Optimization (BFO)-based Feature Selection (FS) technique. In addition, Whale Optimization Algorithm (WOA) with Deep Belief Network (DBN) model is also applied for ASD classification process in which the hyperparameters of DBN model are optimally tuned with the help of WOA. In order to ensure a better ASD diagnostic outcome, a series of simulation process was conducted on ASD dataset.

A Chaotic Oppositional Whale Optimisation Algorithm with Firefly Search for Medical Diagnostics

There is a growing interest in the study development of artificial intelligence and machine learning, especially regarding the support vector machine pattern classification method. This study proposes an enhanced implementation of the well-known whale optimisation algorithm, which combines chaotic and opposition-based learning strategies, which is adopted for hyper-parameter optimisation and feature selection machine learning challenges. The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation. The Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted for simultaneous feature selection and support vector machine hyper-parameter tuning and validated for medical diagnostics by using breast cancer, diabetes, and erythemato-squamous dataset. The performance of the proposed model is compared with multiple competitive support vector machine models boosted with other metaheuristics, including another improved whale optimisation approach, particle swarm optimisation algorithm, bacterial foraging optimisation algorithms, and genetic algorithms. Results of the simulation show that the proposed model outperforms other competitors concerning the performance of classification and the selected subset feature size.

Mel Frequency Cepstral Coefficients based Bacterial Foraging Optimization with DNN-RBF for Speaker Recognition

Objectives: To improve the accuracy and to reduce the time complexity of the Speaker Recognition system using Mel-Frequency Cepstral Coefficients (MFCCs) and Bacterial Foraging optimization (BFO) with DNN –RBF. Method: The MFCCs of each speech sample are derived by pre-processing the audio speech signal. The features are optimized with BFO algorithm. Finally, the probability score for each speaker is generated to identify the speaker. Then the features are classified towards the target speaker using DNN-RBF. For the proposed MBFOB speaker recognition function, the TIMIT read corpus is used. It contains a total of 6300 phrases, 10 phrases each. Findings: the identity of user is validated in the fields of authentication and surveillance for recognition of speaker. By using the audio speech signal, features are extracted. This paper suggests an MBFOB solution based on Mel-frequency Cepstral Coefficients and DNN-RBF with BFO, for the identification of speakers. The speech utterance from the TIMIT data corpus is preprocessed to obtain MFCC feature vectors DNN-RBF is used for the purpose of classifying the speaker and the feature vectors in the output layers are optimized with Bacterial Foraging optimization. Finally, the scores for each speaker are calculated to identify the speaker. Different output metrics like EER, DCF, Cavg and accuracy are used to test the proposed speaker recognition technique. The execution time of this proposed method is found to be lesser than the other existing methods. The experimental findings are contrasted with other current methods and it shows the efficiency of our approach. Novelty: A novel MFCC-based Bacterial Foraging Optimization with Deep Neural Network-Radial Basis Function (DNN-RBF) for identification of exact speaker is proposed in this study. Keywords: BFO; DNN; RBF; Speech processing; speaker recognition; MFCC extraction; deep neural network; and Bacterial foraging optimization; scoring

Optimal Location of Capacitive Units in Radial Distribution Networks Aiming to Reduce System Losses Using the Exchange Market Algorithm

Distributed generation (DG) has been a significantly popular approach in recent years due to its sought effects. However, DG tends to intensify the harmonic distortion level. In the case of implanting shunt capacitors, disregarding harmonies lead to increased harmonic pollution as a result of intensifying the imposition between the inductive elements of the power system and the shunt capacitors. The present study aims to develop a new minimization method for power loss in systems that are exposed to inequality or equality constraints by optimizing a long term schedule for radial distribution of capacitor banks. We specified the best locations for the installation of capacitor banks via the Voltage Stability Index (VSI) and the new integrated approach of Loss Sensitivity Factor (LSF). Furthermore, we detected the optimum size of the capacitor via the Exchange Market Algorithm (EMA). To examine the proposed method, we used 34-bus and 85-bus networks considering load variations via 1% step sizes in the range of light load (50%) to peak load (160%) while monitoring the optimization process during the entire period of testing. The comparison of the obtained results with the particle swarm optimization (PSO), Bacterial Foraging Optimization (BFO), and Water Cycle Algorithm (WCA) revealed improvement of the voltage profile together with a remarkable reduction in active power loss.

A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends

Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications. In the past decades, numerous swarm intelligence algorithms have been developed, including ant colony optimization (ACO), particle swarm optimization (PSO), artificial fish swarm (AFS), bacterial foraging optimization (BFO), and artificial bee colony (ABC). This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures. It provides an overview of the various swarm intelligence algorithms and their advanced developments, and briefly provides the description of their successful applications in optimization problems of engineering fields. Finally, opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.

Evolutionary state‐based novel multi‐objective periodic bacterial foraging optimization algorithm for data clustering

AbstractClustering divides objects into groups based on similarity. However, traditional clustering approaches are plagued by their difficulty in dealing with data with complex structure and high dimensionality, as well as their inability in solving multi‐objective data clustering problems. To address these issues, an evolutionary state‐based novel multi‐objective periodic bacterial foraging optimization algorithm (ES‐NMPBFO) is proposed in this article. The algorithm is designed to alleviate the high‐computing complexity of the standard bacterial foraging optimization (BFO) algorithm by introducing periodic BFO. Moreover, two learning strategies, global best individual (gbest) and personal historical best individual (pbest), are used in the chemotaxis operation to enhance the convergence speed and guide the bacteria to the optimum position. Two elimination‐dispersal operations are also proposed to prevent falling into local optima and improve the diversity of solutions. The proposed algorithm is compared with five other algorithms on six validity indexes in two data clustering cases comprising nine general benchmark datasets and four credit risk assessment datasets. The experimental results suggest that the proposed algorithm significantly outperforms the competing approaches. To further examine the effectiveness of the proposed strategies, two variants of ES‐NMPBFO were designed, and all three forms of ES‐NMPBFO were tested. The experimental results show that all of the proposed strategies are conducive to the improvement of solution quality, diversity and convergence.