Bacterial Foraging Optimization Algorithm Research Articles

An Improved Bacterial Foraging Algorithm for Multi-modal Problems

In order to improve the performance of BFO (bacterial foraging optimization) algorithm in multi-modal problems, the algorithm is optimized and improved in this paper. In the chemotaxis operation, the gradient accumulation variable swimming step size is realized, and the convergence speed of the algorithm is accelerated. In the reproduction operation, a new population competition mechanism is added, and a double-index sorting is realized, which improves the ability of the algorithm to jump out of the local optimum and the diversity of the population. In the migration operation, the vitality index of bacteria is added to dynamically affect the migration probability, ensure the survival probability of elite individuals, and further improve the global optimization ability of the algorithm. Finally, a multi-modal function is selected to test. Compared with the genetic algorithm, the improved BFO algorithm is better than the genetic algorithm in speed and quality.

Find optimal capacity and location of distributed generation units in radial distribution networks by using enhanced coyote optimization algorithm

This paper proposes a novel effective optimization algorithm called enhanced coyote optimization algorithm (ECOA). This proposed method is applied to optimally select the position and capacity of distributed generators (DGs) in radial distribution networks. It is a multi-objective optimization problem where properly installing DGs should simultaneously reduce the power loss, operating costs as well as improve voltage stability. Based on the original coyote optimization algorithm (COA), ECOA is developed to be able to expand the search area and retain a good solution group in each generation. It includes two modifications to improve the efficiency of the original COA approach where the first one is replacing the central solution by the best current solution in the first new solution generation technique and the second focuses on reducing the computation burden and process time in the second new solution generation step. In this research, various experiments have been implemented by applying ECOA, COA as well as salp swarm algorithm (SSA), Sunflower optimization (SOA) for three IEEE radial distribution power networks with 33, 69 and 85 buses. Obtained results have been statistically analyzed to investigate the appropriate control parameters and to verify the performance of the proposed ECOA method. In addition, the performance of ECOA is also compared to various similar meta-heuristic methods such as genetic algorithm (GA), particle swarm optimization (PSO), hybrid genetic algorithm and particle swarm optimization (HGA-PSO), simulated annealing, bacterial foraging optimization algorithm, backtracking search optimization algorithm, harmony search algorithm, whale optimization algorithm (WOA) and combined power loss index-whale optimization algorithm (PLI-WOA). Detailed comparisons show that ECOA can determine more effective location and size of DGs with faster speed than other methods. Specifically, the improvement levels of the proposed method over compared to SFO, SSA, and COA can be up to 2.1978%, 0.7858% and 0.2348%. Furthermore, as compared to other existing methods in references, ECOA achieves the significant improvements which are up to 31.7491%, 20.2143% and 22.7213% for the three test systems, respectively. Thus, the proposed method is a favorable method in solving the optimal determination of DGs in radial distribution networks.

Stability enhancement of induction generator–based series compensated wind power plants by alleviating subsynchronous torsional oscillations using BFOA‐optimal controller tuned STATCOM

AbstractIn this paper, a STATCOM (static synchronous compensator) is used to encounter the potential SSR (subsynchronous resonance) observed by IG (Induction Generator) based series compensated wind farms. The basic controller used for STATCOM control is identical to that of the literature. An idea of a unique meta‐heuristic swarm‐based optimization technique called BFOA (bacterial foraging optimization algorithm) based optimal‐controller is introduced for optimal parameter selection of the basic controller used in the control scheme of the STATCOM. The investigation is carried out with 500 MW IG‐based wind farm exposed to three‐phase LLL‐G fault close to the PCC (point of common coupling) and implemented with MATLAB in both steady and transient states for the three different cases, namely, without STATCOM, with the basic STATCOM controller, and in the presence of the proposed BFOA‐optimal controller‐based STATCOM. In both the states, the observed eigenvalues of the test system, together with the time domain results of the generator rotor dynamics for three distinct cases, reveals the effectiveness of the suggested BFOA‐optimal controller tuned STATCOM in mitigating the potential SSR.

Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy.

Bacterial foraging optimization (BFO) algorithm is a novel swarm intelligence optimization algorithm that has been adopted in a wide range of applications. However, at present, the classical BFO algorithm still has two major drawbacks: one is the fixed step size that makes it difficult to balance exploration and exploitation abilities; the other is the weak connection among the bacteria that takes the risk of getting to the local optimum instead of the global optimum. To overcome these two drawbacks of the classical BFO, the BFO based on self-adaptive chemotaxis strategy (SCBFO) is proposed in this paper. In the SCBFO algorithm, the self-adaptive chemotaxis strategy is designed considering two aspects: the self-adaptive swimming based on bacterial search state features and the improvement of chemotaxis flipping based on information exchange strategy. The optimization results of the SCBFO algorithm are analyzed with the CEC 2015 benchmark test set and compared with the results of the classical and other improved BFO algorithms. Through the test and comparison, the SCBFO algorithm proves to be effective in reducing the risk of local convergence, balancing the exploration and the exploitation, and enhancing the stability of the algorithm. Hence, the major contribution in this research is the SCBFO algorithm that provides a novel and practical strategy to deal with more complex optimization tasks.

An A*-based Bacterial Foraging Optimisation Algorithm for Global Path Planning of Unmanned Surface Vehicles

The bacterial foraging optimisation (BFO) algorithm is a commonly adopted bio-inspired optimisation algorithm. However, BFO is not a proper choice in coping with continuous global path planning in the context of unmanned surface vehicles (USVs). In this paper, a grid partition-based BFO algorithm, named AS-BFO, is proposed to address this issue in which the enhancement is contributed by the involvement of the A* algorithm. The chemotaxis operation is redesigned in AS-BFO. Through repeated simulations, the relative optimal parameter combination of the proposed algorithm is obtained and the most influential parameters are identified by sensitivity analysis. The performance of AS-BFO is evaluated via five size grid maps and the results show that AS-BFO has advantages in USV global path planning.

Improved Motion Planning of Humanoid Robots Using Bacterial Foraging Optimization

SUMMARYThis paper emphasizes on Bacterial Foraging Optimization Algorithm for effective and efficient navigation of humanoid NAO, which uses the foraging quality of bacteria Escherichia coli for getting shortest path between two locations in minimum time. The Gaussian cost function assigned to both attractant and repellent profile of bacterium performs a major role in obtaining the best path between any two locations. Mathematical formulations have been performed to design the control architecture for humanoid navigation using the proposed methodology. The developed approach has been tested in a simulation platform, and the simulation results have been validated in an experimental platform. Here, motion planning for both single and multiple humanoid robots on a common platform has been performed by integrating a petri-net architecture for multiple humanoid navigation. Finally, the results obtained from both the platforms are compared in terms of suitable navigational parameters, and proper agreements have been observed with minimal amount of error limits.

Research on Low-energy Adaptive Clustering Hierarchy Protocol based on Multi-objective Coupling Algorithm

Wireless Sensor Networks (WSN) is a distributed Sensor network whose terminals are sensors that can sense and check the environment. Sensors are typically battery-powered and deployed in where the batteries are difficult to replace. Therefore, maximize the consumption of node energy and extend the network’s life cycle are the problems that must to face. Low-energy adaptive clustering hierarchy (LEACH) protocol is an adaptive clustering topology algorithm, which can make the nodes in the network consume energy in a relatively balanced way and prolong the network lifetime. In this paper, the novel multi-objective LEACH protocol is proposed, in order to solve the proposed protocol, we design a multi-objective coupling algorithm based on bat algorithm (BA), glowworm swarm optimization algorithm (GSO) and bacterial foraging optimization algorithm (BFO). The advantages of BA, GSO and BFO are inherited in the multi-objective coupling algorithm (MBGF), which is tested on ZDT and SCH benchmarks, the results are shown the MBGF is superior. Then the multi-objective coupling algorithm is applied in the multi-objective LEACH protocol, experimental results show that the multi-objective LEACH protocol can greatly reduce the energy consumption of the node and prolong the network life cycle.

Web Facilitated Anthracnose Disease Segmentation from the Leaf of Mango Tree Using Radial Basis Function (RBF) Neural Network

In this article, we present an automatic web facilitated leave disease segmentation system for mango tree using a neural network (NN). The proposed system compromised of four major steps. First, the real-time images of the mango leaves are acquired using the digital camera enabled with the web. Second, the images are preprocessed and features are extracted using a scale-invariant feature transform method. Third, the training of the NN is optimized with bacterial foraging optimization algorithm using the most dissimilar features. Finally, the radial basis function NN is used for the extraction of the diseased region from the mango leave images. The experimental results validated the high-level accuracy of the proposed system for the segmentation of anthracnose (fungal) disease obtaining an average Specificity = 0.9115 and Sensitivity = 0.9086. A comparison with other states of art methods is also presented, and some relevant future developments are also offered. This presented system is intuitive, user-friendly and is being developed to be espoused in precision agriculture.

Modified artificial bee colony algorithms for solving multiple circle detection problem

Determining circular shapes from digital images is one of the most required operations in computer vision and its applications. Various techniques including evolutionary and swarm intelligence-based algorithms have been introduced and successfully used over the past decade for detecting single or multiple circles. In this study, two different circle candidate list generation approaches that are based on generating a combination between abandoned food sources and the obtained food sources in the final colony have been proposed and integrated into the workflow of the standard implementation of the artificial bee colony (ABC) algorithm. Experimental studies on a set of real and synthetic images showed that the proposed list generation approaches improved the solving capabilities of ABC algorithm and decreased the total error values related to the discovered circles compared to the ABC-based implementation for which a circle or circles are selected from a candidate list containing only abandoned food sources, genetic algorithm, bacterial foraging optimization algorithm and an improved version of the Hough transform-based circle detection technique called randomized Hough transform.

An Approach for Image Search and Retrieval by Cluster-Based Indexing of Binary MKSIFT Codes

Abstract Image search is an information retrieval approach that gained remarkable attention in the areas like multimedia and computer vision. The first work presented a cross-indexing-based approach for image retrieval using multiple kernel scale-invariant feature transform (MKSIFT), where the key point descriptor was calculated using two kernel functions. Previous work had complexity issues while dealing with the large databases, and hence, to avoid this, cluster-based indexing of binary MKSIFT codes is presented. The proposed cluster-based indexing scheme uses the MKSIFT feature extraction and the Particle Swarm-Fractional Bacterial foraging optimization algorithm for extracting the useful features from the images. Also, the Bayesian fuzzy clustering scheme is employed for grouping the images in the database into several clusters. The search index is constructed for the user query, and Bhattacharya distance between the cluster centroids and the search index is calculated to identify the optimal cluster. Then, finally, the images present in the optimal centroid are retrieved. The experimentation of the proposed cluster-based indexing scheme is analysed for the various query from the users. From the analysis, it is evident that the proposed cluster-based indexing scheme has achieved improved performance with the mean values of 0.9656, 0.9489 and 0.9049, for precision, recall and F measure, respectively.

Incorporating Particle Swarm Optimization into Improved Bacterial Foraging Optimization Algorithm Applied to Classify Imbalanced Data

In this paper, particle swarm optimization is incorporated into an improved bacterial foraging optimization algorithm, which is applied to classifying imbalanced data to solve the problem of how original bacterial foraging optimization easily falls into local optimization. In this study, the borderline synthetic minority oversampling technique (Borderline-SMOTE) and Tomek link are used to pre-process imbalanced data. Then, the proposed algorithm is used to classify the imbalanced data. In the proposed algorithm, firstly, the chemotaxis process is improved. The particle swarm optimization (PSO) algorithm is used to search first and then treat the result as bacteria, improving the global searching ability of bacterial foraging optimization (BFO). Secondly, the reproduction operation is improved and the selection standard of survival of the cost is improved. Finally, we improve elimination and dispersal operation, and the population evolution factor is introduced to prevent the population from stagnating and falling into a local optimum. In this paper, three data sets are used to test the performance of the proposed algorithm. The simulation results show that the classification accuracy of the proposed algorithm is better than the existing approaches.

Nonlinear process control of anaerobic sludge digester

Anaerobic absorption is a sequence of processes by which microbes separate decomposable wastes without oxygen. They convert the decomposable wastes in the form of carbon dioxide and water. Due to the nonlinear nature of the anaerobic process several control strategies are used to adjust the manipulated inputs to maintain the process outputs at desired set points. The objective of this work is to achieve the desired sludge temperature level of the anaerobic digester using PI (Proportional Integral), PID (Proportional-Integral-Derivative) Control and Fuzzy Logic Control (FLC). The comparative study indicates that the Fuzzy Logic Control is enhanced over conventional PID Control. The designed system can also be implemented using BFOA (Bacterial Foraging Optimization Algorithm) to optimize the operation and in tuning of PID controller parameters for an anaerobic digester.

A New Reliability Rock Mass Classification Method Based on Least Squares Support Vector Machine Optimized by Bacterial Foraging Optimization Algorithm

Classification of the surrounding rock is the basis of tunnel design and construction. However, conventional classification methods do not allow dynamic tunnel construction adjustments because they are time‐consuming and do not consider the randomness of rock mass. This paper presents a new reliability rock mass classification method based on a least squares support vector machine (LSSVM) optimized by a bacterial foraging optimization algorithm (BFOA). The LSSVM is adopted to express the implicit relationship between classification indicators and rock mass grades, which is a response surface function for reliability evaluation. LSSVM parameters were optimized by the BFOA to form a hybrid BFOA‐LSSVM algorithm. Using geological prediction and rock strength resilience results as classification indicators, samples were developed to train the LSSVM model using the hybrid algorithm. The Monte Carlo sampling method of reliability classification was implemented and applied to the Suqiao tunnel at the Puyan highway in the Fujian province of China; the influence of parameters on the performance of the algorithm is discussed. The results indicate that the new method is feasible for tunnel engineering; it can improve the classification accuracy of surrounding rock exhibiting randomness, to provide an effective means of classifying surrounding rock in the dynamic design of tunnel construction.

Adaptive beam forming of MIMO system using optimal steering vector with hybrid bacterial foraging optimisation algorithm for channel selection

The modern communication systems face several issues that are quite challenging, more so in those involving smart antennas such as the antenna array beam. In this work, an optimised bacterial foraging optimisation algorithm (BFOA) along with a greedy algorithm and a Tabu search (TS) algorithm for the channel selection having an adaptive beam which is formed by using a steering vector is proposed. In the proposed hybrid, adaptive seed dispersion will make the BFOA-TS be able to converge faster when compared to a BFOA-Greedy. This type of behaviour has been duly verified by means of an application of the BFOA-TS and the BFOA-Greedy on the test functions which are well-known. The results of this experiment proved that the method proposed was able to achieve a better performance compared to that of the others.

An Intelligent Integrated Approach for Efficient Demand Side Management With Forecaster and Advanced Metering Infrastructure Frameworks in Smart Grid

The development of advanced metering infrastructure (AMI) in smart grid (SG) had enabled consumers to participate in demand-side management (DSM) using the price-based demand response (DR) programs offered by the distribution companies (DISCO). This way, not only the consumers minimize their electricity bills and discomfort, but also the DISCOs can handle peak power demand and reduce the carbon (CO 2 ) emissions in a controlled manner. Building an optimization framework that will minimize cost, peak demand, waiting time, and CO 2 emission is not only a challenging task but also a concern of DSM. Most analyses are based on cost and peak-to-average ratio (PAR) minimization, but the effectiveness of the DSM framework is equally determined by user comfort and CO 2 emission. Considering only one objective (cost) or two objectives (cost and PAR) is not sufficient. Thus, for DSM framework to achieve these four relatively independent objectives at the same time, minimized cost, PAR, CO 2 emission, and user discomfort, an energy management controller (EMC) based on our proposed algorithm hybrid bacterial foraging and particle swarm optimization (HBFPSO) is employed that return optimal power usage schedule for consumers. A novel DSM framework consists of four units: (i) DISCO, (ii) multi-layer perceptron (MLP) based forecast engine, (iii) AMI, and (iv) demand-side energy management modules is successfully developed in this work. To validate the proposed model, extensive simulations are conducted and results are compared with the benchmark models like genetic algorithm (GA), bacterial foraging optimization algorithm (BFOA), binary particle swarm optimization (BPSO), and a hybrid combination of genetic and binary particle swarm optimization (GBPSO) in terms of electricity cost, PAR, user comfort, and CO 2 emissions. The simulation results demonstrate effectiveness of our proposed model to outperform all the benchmark models in optimizing the consumer and DISCO objectives. The proposed scheme has reduced electricity cost, user discomfort, PAR, and CO 2 emission for the residential sector by 15.14%, 4.6%, 61.6%, and 52.86% in scenario 1, 62.60%, 4.56%, 60.77%, and 27.77% in scenario 2, and 26.03%, 4.54%, 63.78%, and 23.02% in scenario 3, as compared to without an EMC. Similarly, for commercial sector the proposed HBFPSO algorithm reduces electricity cost, user discomfort, PAR, and CO 2 emission by 11.31%, 5.5%, 60.9%, and 38.18% in scenario 1, 64.9%, 5.56%, 44.08%, and 58.8% in scenario 2, 15.31%, 5.26%, 78.22%, and 15.58% in scenario 3. Likewise, the proposed algorithm also has superior performance for the industrial sector for all the three scenarios.

A hybrid BFO-FOA-based energy efficient cluster head selection in energy harvesting wireless sensor network

This paper proposed a hybrid bacterial foraging optimisation (BFO) and fruitfly optimisation algorithm (FOA) for energy efficient cluster head (CH) selection in wireless sensor network. The bacterial foraging optimisation algorithm is inspired by the group foraging behaviour of bacteria such as E. coli and M. xanthus realising chemistry in the environment and moving away from specific signals. The FOA is simple framework and easy to implement for solving an optimisation problem with different characteristics. It is robust and fast algorithm and used to solve discrete optimisation problems. The performance metrics of the proposed method is evaluated for end to end delay, packet delivery, drop ratio, energy consumption, network lifetime and throughput. The simulation results show that the proposed method achieves better energy efficiency and network lifetime of 35%, 58%, and 67% compared to existing methods like ant colony optimisation, particle swarm optimisation and genetic algorithm.

Quantum Bacterial Foraging Optimization: From Theory to MIMO System Designs

This article develops a quantum bacterial foraging optimization (QBFO) algorithm, a quantum intelligence algorithm based on quantum computing and bacterial foraging optimization (BFO), with application in MIMO system optimization designs. In QBFO, a multiqubit is used to represent a bacterium, and a quantum rotation gate is used to mimic chemotaxis. Because the quantum bacterium with multiqubit has the advantage that it can represent a linear superposition of states (binary solutions) in search space probabilistically, the proposed QBFO algorithms shows better performance on solving combinatorial optimization problems than its classical counterpart BFO and Quantum Genetic Algorithm (QGA), especially for parallel non-gradient optimization. A sparse channel estimation scheme based on QBFO with adaptive phase rotation (AQBFO) in 3D MIMO system is proposed, and simulation results show that AQBFO achieved a better performance than existing algorithms including least squares (LS), iteratively reweighted least squares (IRLS), matching pursuit (MP), and orthogonal matching pursuit (OMP). We further improve some critical aspects such as reproduction and dispersal processes of AQBFO, propose an improved IQBFO algorithm, and apply it for interference coordination in 3D multi-cell multi-user MIMO systems, aiming to maximize the spectral efficiency. It considers user fairness and jointly optimizes cell-center and cell-edge user specific antenna downtilts and power to maximize each user's sum rate. This problem is a combinatorial non-convex optimization problem that cannot be solved by the traditional Karush-Kuhn-Tucker Lagrangian algorithm whereas the IQBFO algorithm solves it effectively.

Adaptive beam forming of MIMO system using optimal steering vector with hybrid bacterial foraging optimisation algorithm for channel selection

Adaptive beam forming of MIMO system using optimal steering vector with hybrid bacterial foraging optimisation algorithm for channel selection

Modeling and optimization of urban rail transit scheduling with adaptive fruit fly optimization algorithm

Abstract Despite the rapid development of urban rail transit in China, there are still some problems in train operation, such as low efficiency and poor punctuality. To realize a proper allocation of passenger flows and increase train frequency, this paper has proposed an improved urban rail transit scheduling model and solved the model with an adaptive fruit fly optimization algorithm (AFOA). For the benefits of both passengers and operators, the shortest average waiting time of passengers and the least train frequency are chosen as the optimization objective, and train headway is taken as the decision variable in the proposed model. To obtain higher computational efficiency and accuracy, an adaptive dynamic step size is built in the conventional FOA. Moreover, the data of urban rail transit in Zhengzhou was simulated for case study. The comparison results reveal that the proposed AFOA exhibits faster convergence speed and preferable accuracy than the conventional FOA, particle swarm optimization, and bacterial foraging optimization algorithms. Due to these superiorities, the proposed AFOA is feasible and effective for optimizing the scheduling of urban rail transit.

Mathematical modeling and a hybridized bacterial foraging optimization algorithm for the flexible job-shop scheduling problem with sequencing flexibility

The flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem (JSP) in which operations can be performed by a set of candidate capable machines. An extended version of the FJSP, entitled sequencing flexibility, is studied in this work, which considers precedence between the operations in the form of a directed acyclic graph instead of a sequential order. In this work, a mixed integer linear programming (MILP) formulation is presented to minimize weighted tardiness for the FJSP with sequencing flexibility. Due to the NP-hardness of the problem, a novel biomimicry hybrid bacterial foraging optimization algorithm (HBFOA) is developed, which is inspired by the behavior of E. coli bacteria in its search for food. The developed HBFOA search method is hybridized with simulated annealing (SA). Additionally, the algorithm has been enhanced by a local search method based on the manipulation of critical operations. Classical dispatching rules have been employed to create the initial swarm of HBFOA, and a new dispatching rule named minimum number of operations has been devised. The developed approach has been packaged in the form of a decision support system (DSS) developed on top of Microsoft Excel—a tool most small and mid-range enterprises (SME) use heavily for planning. A case study with local industry is presented to validate the proposed HBFOA and MILP. Additional numerical experiments using literature benchmarks are further used for validation. The results demonstrate that the HBFOA outperformed the classical dispatching rules and the best integer solution of MILP when minimizing the weighted tardiness and offered comparable results for the makespan instances.