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This paper introduces an adaptive hybrid approach to address the forward kinematics problem of a Hexa parallel robot (HPR), known for its challenge in obtaining a unique closed-form analytic solution. In the initial stage, we construct an artificial neural network (ANN) model to rapidly generate a preliminary result, effectively narrowing the search space. Subsequently, bacterial foraging optimization (BFO) is adapted to refine the result by focusing on exploration within the reduced search space. Adaptive functions adjust BFO parameters based on the error level in the preliminary result, enhancing algorithm performance. Software is developed to demonstrate the practical application of this method. Experimental results within the robot workspace indicate a significant reduction in calculation errors compared to using only the ANN model. [ABSTRACT FROM AUTHOR]

Dispatch centres are an important part of the feeder bus network, and their location affects the design process of the feeder route. In some remote areas with weak transport infrastructure, it is very important to find an effective tool to simultaneously select the optimal location of the dispatch centre as well as transit routing process, which could improve the performance of the feeder bus system. The purpose of this article is to present an integrated optimization model for joint location and dispatching decisions for Feeder Bus Route Design (FBRD). The proposed methodology can select a number of best dispatch centres in alternative sets and calculate the order of the demand points visited by the feeder route. The objective of the model is to simultaneously minimize the total construction cost of selected dispatch centres and the total operational cost of the designed feeder bus system. The methodology facilitates obtaining solutions using the design of an improved double population Bacterial Foraging Optimization (BFO) algorithm. For example, it redefines the solution coding and the heuristic used to randomly initialize the initial population. When applied to the design of a feeder bus system for a station at Nanjing (China), the results reveal that a reduced budget may lead to change in the location of the dispatch centre; a more distant centre is required, which may increase the total mileage cost of all feeder routes. A detailed comparison of the improved and standard BFO and CPLEX shows that the difference between solutions is acceptable. However, the calculation time is greatly reduced, thus proving the effectiveness of the proposed algorithm.

Feature selection is an important component of the machine learning domain, which selects the ideal subset of characteristics relative to the target data by omitting irrelevant data. For a given number of features, there are 2n possible feature subsets, making it challenging to select the optimal set of features from a dataset via conventional feature selection approaches. We opted to investigate glaucoma infection since the number of individuals with this disease is rising quickly around the world. The goal of this study is to use the feature set (features derived from fundus images of benchmark datasets) to classify images into two classes (infected and normal) and to select the fewest features (feature selection) to achieve the best performance on various efficiency measuring metrics. In light of this, the paper implements and recommends a metaheuristics-based technique for feature selection based on emperor penguin optimization, bacterial foraging optimization, and proposes their hybrid algorithm. From the retinal fundus benchmark images, a total of 36 features were extracted. The proposed technique for selecting features minimizes the number of features while improving classification accuracy. Six machine learning classifiers classify on the basis of a smaller subset of features provided by these three optimization techniques. In addition to the execution time, eight statistically based performance metrics are calculated. The hybrid optimization technique combined with random forest achieves the highest accuracy, up to 0.95410. Because the proposed medical decision support system is effective and ensures trustworthy decision-making for glaucoma screening, it might be utilized by medical practitioners as a second opinion tool, as well as assist overworked expert ophthalmologists and prevent individuals from losing their eyesight. [ABSTRACT FROM AUTHOR]

At present, the traditional channel estimation algorithms have the disadvantages of over-reliance on initial conditions and high complexity. The bacterial foraging optimization (BFO)-based algorithm has been applied in wireless communication and signal processing because of its simple operation and strong self-organization ability. But the BFO-based algorithm is easy to fall into local optimum. Therefore, this paper proposes the quantum bacterial foraging optimization (QBFO)-binary orthogonal matching pursuit (BOMP) channel estimation algorithm to the problem of local optimization. Firstly, the binary matrix is constructed according to whether atoms are selected or not. And the support set of the sparse signal is recovered according to the BOMP-based algorithm. Then, the QBFO-based algorithm is used to obtain the estimated channel matrix. The optimization function of the least squares method is taken as the fitness function. Based on the communication between the quantum bacteria and the fitness function value, chemotaxis, reproduction and dispersion operations are carried out to update the bacteria position. Simulation results show that compared with other algorithms, the estimation mechanism based on QBFO-BOMP algorithm can effectively improve the channel estimation performance of millimeter wave (mmWave) massive multiple input multiple output (MIMO) systems. Meanwhile, the analysis of the time ratio shows that the quantization of the bacteria does not significantly increase the complexity. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the enhancement of voltage stability margin using Unified Power Flow Control (UPFC) device tuned with Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bacterial Foraging Optimization (BFO) and Cuckoo Search techniques (CS) on Nigerian 330 kV 56-bus practical network. In order to have a voltage stable power system, it is good to keep voltages within the acceptable limits. This is achieved using continuation power flow embedded in Power System Analysis Toolbox (PSAT). The optimal location and tuning of the UPFC device is determined using line stability index and the met-heuristics techniques. The effects of UPFC and the proposed optimizing techniques on voltage stability margin are examined. The results so obtained for tuning PSO, ABC, BFO and Cuckoo Search with UPFC device are compared to know the technique that yields the best loading parameter for Nigerian 56- bus power system for voltage stability margin enhancement. It is found that UPFC tuned with Cuckoo Search outperforms the other techniques in terms of the increased loading parameter of the Nigerian power system with margin improvement of 86.6%, 90% and 88.6% for Adiabor (Bus 45), Jalingo (Bus 34) and Jos (Bus 13). The tuned UPFC device has proved beyond reasonable doubt that it can improve voltage stability margin of the entire Nigerian 330 kV Network as envisaged from results before and after optimization.

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.

The Edge detection is a customarily task. Edge detection is the main task to perform as it gives clear information about the images. It is a tremendous device in photograph processing gadgets and computer imaginative and prescient. Previous research has been done on moving window approach and genetic algorithms. In this research paper new technique, Bacterial Foraging Optimization (BFO) is applied which is galvanized through the social foraging conduct of Escherichia coli (E.coli). The Bacterial Foraging Optimization (BFO) has been practice by analysts for clarifying real world optimization problems arising in different areas of engineering and application domains, due to its efficiency. The Brightness preserving bi-histogram equalization (BHEE) is another technique that is used for edge enhancement. The BFO is applied on the low level characteristics on the images to find the pixels of natural images and the values of F-measures, recall(r) and precision (p) are calculated and compared with the previous technique. The enhancement technique i.e. BBHE is carried out to improve the information about the pictures.

This paper proposes an improved Bacterial Foraging Optimization for economically optimal dispatching of the microgrid. Three optimized steps are presented to solve the slow convergence, poor precision, and low efficiency of traditional Bacterial Foraging Optimization. First, the self-adaptive step size equation in the chemotaxis process is present, and the particle swarm velocity equation is used to improve the convergence speed and precision of the algorithm. Second, the crisscross algorithm is used to enrich the replication population and improve the global search performance of the algorithm in the replication process. Finally, the dynamic probability and sine-cosine algorithm are used to solve the problem of easy loss of high-quality individuals in dispersal. Quantitative analysis and experiments demonstrated the superiority of the algorithm in the benchmark function. In addition, this study built a multi-objective microgrid dynamic economic dispatch model and dealt with the uncertainty of wind and solar using the Monte Carlo method in the model. Experiments show that this model can effectively reduce the operating cost of the microgrid, improve economic benefits, and reduce environmental pollution. The economic cost is reduced by 3.79% compared to the widely used PSO, and the economic cost is reduced by 5.23% compared to the traditional BFO. [ABSTRACT FROM AUTHOR]

Summary: This article proposes an intellectual quorum sensing‐centered bacterial horde optimization (QBHO) exert for assessing and authenticating the optimization problems. Quorum detection realizes the finest place of the bacteria by apprehending the most awful point within the hunt area. This mechanism is utilized in this study for problem‐solving. With this bacterial behavior and location perceptive, the superlative optimum result is obtained. In this anticipated QBHO algorithm, the competence of bacteria is enhanced to be beneficial so that results can be enhanced. The fundamental group‐built optimization procedures for instance, particle swarm, ant cluster, biogeography‐built algorithm, simulated bee constellation procedure and typical bacterial herd optimization through the distinctive considerations remain simulated and then associated through the intended practice for its ratification of results with QBHO. The projected QBHO practice is exploited to ensue with the seven‐typical benchmark with unimodal and multimodal trial functions intended to prove its practicability and ideality. The realized findings imply that the suggested QBHO approach surpasses the deemed optimization procedures for complex standard functions. Furthermore, the intended procedure might be appropriate to several engineering challenges, notably intended for intricate concurrent optimization problems. [ABSTRACT FROM AUTHOR]

Effective techniques for reducing traffic congestion include adding new edges to the road network or increasing the capacity of existing edges. To determine the optimal set of improvements that can be made to an existing road network, it is standard practise to solve a Discrete Network Design Problem. Due to the bi-level nature of its formulation, it is one of the most difficult problems to solve to optimality. The Bacterial Foraging Optimization technique has been investigated in a previous work to gauge its effectiveness in determining the n-optimal solution to the Discrete Network Design Problem. This concept is further developed in this paper by hybridizing the Bacterial Foraging Optimization algorithm with Simulated Annealing. In the elimination step, the metropolis criterion from Simulated Annealing ensures a more diverse search of the solution space. We also employ a modified candidate set of improvements that incorporates both edge and capacity additions. The effectiveness of the hybrid bacterial foraging optimization algorithm in determining n-optimal solutions to the Discrete Network Design Problem has been demonstrated by testing it on the 24-node, 76-arc Sioux Falls network.

In this paper, a smart home energy management system is proposed to improve the efficiency of the electricity infrastructure of residential buildings. To solve the scheduling problem of a smart building, we propose bacterial foraging ant colony optimization (HB-ACO). The primary objective of scheduling is to shift load from on-peak hours to off-peak hours to reduce electricity cost and peak-to-average ratio. A comparison of these algorithms is also presented in terms of performance parameters, electricity cost, reduction of PAR, and user comfort in terms of waiting time. The proposed techniques are evaluated using two pricing schemes: (1) time of use and (2) critical peak pricing. Moreover, coordination among home appliances is presented for real-time scheduling. We represent this as a knapsack problem and solve it through ant colony optimization algorithm. The HB-ACO shows better performance than ACO and BFA in reducing electricity cost, PAR, and increased user comfort, which is evident from the simulation results. [ABSTRACT FROM AUTHOR]

Despite that evolutionary and swarm intelligence algorithms have achieved considerable success on multi-objective optimization problems, they face huge challenges when dealing with many-objective optimization problems (MaOPs). There is an urgent call for effective evolutionary and swarm intelligence algorithms for MaOPs. Inspired by the satisfactory performance of bacterial foraging optimization (BFO) on the single-objective optimization problems, this paper extends BFO to deal with MaOPs and proposes a knowledge guided BFO for MaOPs (called as KLBFO). Firstly, KLBFO learns promising direction knowledge based on group decision making idea to guide the population to converge toward proper directions. Secondly, KLBFO learns elite knowledge by a new biological mechanism to accelerate the population to converge. Thirdly, KLBFO learns density knowledge by a new diversity management strategy based on orthogonal grid to produce well-distributed solutions. The performance of KLBFO is comprehensively evaluated by comparing it with eight state-of-the-art algorithms on two suites of test problems and one real-world problem. The empirical results have validated the superior performance of KLBFO for MaOPs. [ABSTRACT FROM AUTHOR]

Data clustering is a technique for dividing data objects into groups based on their similarity. K-means is a simple, effective algorithm for clustering. But, k-means tends to converge to local optima and depends on the cluster's initial values. To address the shortcomings of the k-means algorithm, many nature-inspired techniques have been used. This paper is offered an improved version of bacterial colony optimization (BCO) based on opposition-based learning (OBL) algorithm called OBL + BCO for data clustering. An OBL is used to increase the speed of the convergence rate and searching ability of BCO by computing the opposite solution to the present solution. The strength of the proposed data clustering technique is evaluated using several well-known UCI benchmark datasets. Different performance measures are considered to analyze the strength of the proposed OBL + BCO such as Rand index, Jaccard index, Beta index, Distance index, Objective values, and computational time. The experimental results demonstrated that the proposed OBL + BCO data clustering technique outperformed other data clustering techniques. [ABSTRACT FROM AUTHOR]

This research presents the design of time-modulated antenna arrays with UWB performance. The antenna arrays consider a linear topology with eight UWB disk-notch patch antennas. The technological problem is to find out the optimum antenna positions and/or time sequences to reduce the side lobes and the sidebands in all of the UWB frequency ranges. The design process is formulated as a bacterial foraging optimization. The results show that the uniform array generates a better SLL performance whereas the non-uniform array obtains a wider bandwidth. The uniform array obtains an SLL < −20 dB from 3.37 GHz to 4.8 GHz and the non-uniform array generates an SLL < −7 dB from 2.97 GHz to 5.26 GHz. The sideband levels are very similar for both cases with a value of around −17 dB. [ABSTRACT FROM AUTHOR]

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 EHCDLHIC 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. [ABSTRACT FROM AUTHOR]

With the emergence of Wireless Sensor Networks (WSNs), a large number of academics have worked over the last several decades to increase energy efficiency and clustering. Several clustering algorithm techniques, including optimization-based, fuzzy logic-based, and threshold-based, were created to minimize energy consumption and improve network performance. Optimization algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO), and their variants are presented. But the challenge of selecting the efficient Cluster Head (CH) and cluster formation around it with minimal overhead and energy consumption remains the same. We propose a novel energy-efficient and lightweight clustering technique for WSNs based on the Modified Bacterial Foraging Optimization Algorithm (MBFA). In this study, the goal of developing the MBFA is to reduce energy consumption, communication overhead, and enhance network performance. The MBFA-based CH selection procedure is based on a unique fitness function. The fitness function computes essential characteristics such as remaining energy, node degree, and distance from sensor node to Base Station (BS). Using the fitness value, the MBFA identifies the sensor node as CH. To justify efficiency, the suggested clustering protocol is simulated and tested against state-of-the-art protocols. [ABSTRACT FROM AUTHOR]

The Edge detection is a customarily task. Edge detection is the main task to perform as it gives clear information about the images. It is a tremendous device in photograph processing gadgets and computer imaginative and prescient. Previous research has been done on moving window approach and genetic algorithms. In this research paper new technique, Bacterial Foraging Optimization (BFO) is applied which is galvanized through the social foraging conduct of Escherichia coli (E.coli). The Bacterial Foraging Optimization (BFO) has been practice by analysts for clarifying real world optimization problems arising in different areas of engineering and application domains, due to its efficiency. The Brightness preserving bi-histogram equalization (BHEE) is another technique that is used for edge enhancement. The BFO is applied on the low level characteristics on the images to find the pixels of natural images and the values of F-measures, recall(r) and precision (p) are calculated and compared with the previous technique. The enhancement technique i.e. BBHE is carried out to improve the information about the pictures. [ABSTRACT FROM AUTHOR]

The increasing demand for food production with limited available water resources poses a threat to agricultural activities. Conventional optimization algorithms increase the processing stage and perform in the space allocated from user. Therefore, the proposed work was used to design better performance results. The conjunctive allocation of water resources maximizes the net benefit of farmers. In this study, the novel hybrid optimization model developed is the first of its kind and was designed to resolve the sharing of water resource conflict among different reaches based on a genetic algorithm (GA), bacterial foraging optimization (BFO) and ant colony optimization (ACO) to maximize the net benefit of the water deficit in Sathanur reservoir command. The GA-based optimization model considered crop-related physical and economic parameters to derive optimal cropping patterns for three different conjunctive use policies and further allocation of surface and groundwater for different crops are enhanced with the BFO. The allocation of surface and groundwater for the head, middle and tail reaches obtained from the BFO is considered as an input to the ACO as a guiding mechanism to attain an optimal cropping pattern. Comparing the average productivity values, policy 3 (3.665 Rs/m3) has better values relating to policy 1 (3.662 Rs/m3) and policy 2 (3.440 Rs/m3). Thus, the developed novel hybrid optimization model (GA-BFO-ACO) is very promising for enhancing farmer's net income and can be replicated in other irrigated regions to overcome chronic water problems. The productivity value of policy 3 was 6.54% greater than that of policy 2, whereas that of policy 1 was 6.45% greater. Overall, the comparison shows that the better performance analysis of various optimization was done successfully. HIGHLIGHTS Hybrid optimization model using three meta-heuristic algorithms (GA, BFO and ACO) was developed.; The developed hybrid model is used for optimizing the cropping area, conjunctive water allocation and net returns for different reaches.; The net returns from the developed hybrid model (GA-BFO-ACO) outperform GA- BFO model.; The developed hybrid model is the first of its kind to unravel the sharing of water resources for profit maximization which can be adopted globally to overcome chronic water problems.;

In this paper, a metaheuristic solution algorithm for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with discounted cash flows (MRCPSPDC) is proposed. This problem consists of determining a schedule such that the project is completed, maximizing the project's net present value (NPV) while complying with the delivery deadline. The adaptative bacterial foraging optimization (ABFO) algorithm is a variation of the original bacterial foraging optimization (BFO), which is a nature-inspired metaheuristic optimization algorithm. We implement a version of the chemotactic operator based on a double justification of the activities given the cash flow. This metaheuristic has been tested in the PSPLIB and MMLIB benchmark datasets available in the literature with promising results. Our ABFO algorithm shows excellent performance in all tested instances and provides suitable solutions for the MRCPSP maximizing the NPV. [ABSTRACT FROM AUTHOR]

For the 4-SPU parallel mechanism, the complex multivariate nonlinear equation of the forward kinematics solution is transformed into a minimization problem. This article proposes a new bacterial foraging optimization algorithm based on normal cloud model to obtain the solution of the forward kinematics. Adaptive dynamic adjustment of walking step size of normal forward cloud generator is adopted to improve the fixed step size. Y conditional cloud generator is used to dynamically adjust the migration probability. The simulation results show that new algorithm is an effective method to solve the forward solution optimization with high precision and high speed. [ABSTRACT FROM AUTHOR]