Fractional-Order Control of a Wind Turbine Using Manta Ray Foraging Optimization

In this research paper,an improved strategy to enhance the performance of the DC-link voltage loop regulation in a Doubly Fed Induction Generator(DFIG)based wind energy system has been proposed.The proposed strategy used the robust Fractional-Order(FO)Proportional-Integral(PI)control technique.The FOPI control contains a non-integer order which is preferred over the integer-order control owing to its benefits.It offers extra flexibility in design and demonstrates superior outcomes such as high robustness and effectiveness.The optimal gains of the FOPI controller have been determined using a recent Manta Ray Foraging Optimization(MRFO)algorithm.During the optimization process,the FOPI controller’s parameters are assigned to be the decision variables whereas the objective function is the error racking that to be minimized.To prove the superiority of the MRFO algorithm,an empirical comparison study with the homologous particle swarm optimization and genetic algorithm is achieved.The obtained results proved the superiority of the introduced strategy in tracking and control performances against various conditions such as voltage dips and wind speed variation.

Improved Bacterial Foraging Optimization Algorithm Based on Fuzzy Control Rule Base

Manual construction of a rule base for a fuzzy system is the hard and time-consuming task that requires expert knowledge.In this paper we proposed a method based on improved bacterial foraging optimization（IBFO）,which simulates the foraging behavior of “E.coli” bacterium,to tune the Gaussian membership functions parameters of an improved Takagi-Sugeno-Kang fuzzy system（C-ITSKFS） rule base.To remove the defect of the low rate of convergence and prematurity,three modifications were produced to the standard bacterial foraging optimization（BFO）.As for the low accuracy of finding out all optimal solutions with multi-method functions,the IBFO was performed.In order to demonstrate the performance of the proposed IBFO,multiple comparisons were made among the BFO,particle swarm optimization（PSO）,and IBFO by MATLAB simulation.The simulation results show that the IBFO has a superior performance.

An Analysis of Foraging and Echolocation Behavior of Swarm Intelligence Algorithms in Optimization: ACO, BCO and BA

Optimization techniques are stimulated by Swarm Intelligence wherever the target is to get a decent competency of a problem. The knowledge of the behavior of animals or insects has a variety of models in Swarm Intelligence. Swarm Intelligence has become a potential technique for evolving many robust optimization problems. Researchers have developed various algorithms by modeling the behaviors of the different swarm of animals or insects. This paper explores three existing meta-heuristic methods named as Ant Colony Optimization (ACO), Bee Colony Optimization (BCO) and Bat Algorithm (BA). Ant Colony Optimization was stimulated by the nature of ants. Bee Colony Optimization was inspired by the plundering behavior of honey bees. Bat Algorithm was emerged on the echolocation characteristics of micro bats. This study analyzes the problem-solving behavior of groups of relatively simple agents wherein local interactions among agents, are either directly or indirectly through the environment. The scope of this paper is to explore the characteristics of swarm intelligence as well as its advantages, limitations and application areas, and subsequently, to explore the behavior of ants, bees and micro bats along with its most popular variants. Furthermore, the behavioral comparison of these three techniques has been analyzed and tried to point out which technique is better for optimization among them in Swarm Intelligence. From this, the paper can help to understand the most appropriate technique for optimization according to their behavior.

Parameters Influencing the Optimization Process in Airborne Particles PM10 Using a Neuro-Fuzzy Algorithm Optimized with Bacteria Foraging (BFOA)

The airborne pollutants monitoring is an overriding task for humanity given that poor quality of air is a matter of public health, causing issues mainly in the respiratory and cardiovascular systems, specifically the PM10 particle. In this contribution is generated a base model with an Adaptive Neuro Fuzzy Inference System (ANFIS) which is later optimized, using a swarm intelligence technique, named Bacteria Foraging Optimization Algorithm (BFOA). Several experiments were carried with BFOA parameters, tuning them to achieve the best configuration of said parameters that produce an optimized model, demonstrating that way, how the optimization process is influenced by choice of the parameters.

Manta Ray Foraging Optimization with Machine Learning Based Biomedical Data Classification

The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various sources.The developments of artificial intelligence(AI)and machine learning(ML)models assist in the effectual design of medical data classification models.Therefore,this article concentrates on the development of optimal Stacked Long Short Term Memory Sequence-toSequence Autoencoder(OSAE-LSTM)model for biomedical data classification.The presented OSAE-LSTM model intends to classify the biomedical data for the existence of diseases.Primarily,the OSAE-LSTM model involves min-max normalization based pre-processing to scale the data into uniform format.Followed by,the SAE-LSTM model is utilized for the detection and classification of diseases in biomedical data.At last,manta ray foraging optimization(MRFO)algorithm has been employed for hyperparameter optimization process.The utilization of MRFO algorithm assists in optimal selection of hypermeters involved in the SAE-LSTM model.The simulation analysis of the OSAE-LSTM model has been tested using a set of benchmark medical datasets and the results reported the improvements of the OSAELSTM model over the other approaches under several dimensions.

An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique

Power transformers in transmission network are utilized for increasing or decreasing the voltage level. Power Transformers fail to connect directly to the consumers that result in the less load fluctuations. Power transformer operation under any abnormal condition decreases the lifetime of the transformer. Power Transformer protection from inrush and internal fault is critical issue in power system because the obstacle lies in the precise and swift distinction between them. Due to the limitation of heterogeneous resources, occurrence of fault poses severe problem. Providing an efficient mechanism to differentiate between faults (i.e. inrush and internal) is the key for efficient information flow. In this paper, the task of detecting inrush and internal fault in power transformers is formulated as an optimization problem which is solved by using Hyperbolic S-Transform Bacterial Foraging Optimization (HS-TBFO) technique. The Gaussian Frequency- based Hyperbolic S-Transform detects the faults at much earlier stage and therefore minimizes the computation cost by applying Cosine Hyperbolic S-Transform. Next, the Bacterial Foraging Optimization (BFO) technique has been proposed and has demonstrated the capability of identifying the maximum number of faults covered with minimum test cases and therefore improving the fault detection efficiency in a wise manner. The HS-TBFO technique is evaluated and validated in various simulation test cases to detect inrush and internal fault in a significant manner. This HS-TBFO technique is investigated based on three phase power transformer embedded in a power system fed from both ends. Results have confirmed that the HS-TBFO technique is capable of categorizing the inrush and internal faults by identifying maximum number of faults with minimum computation cost as compared to the state-of-the-art works.

Solving Optimal Power Flow Using Modified Bacterial Foraging Algorithm Considering FACTS Devices

In this paper, a new Modified Bacterial Foraging Algorithm (MBFA) method is developed to incorporate FACTS devices in optimal power flow (OPF) problem. This method can provide an enhanced economic solution with the use of controllable FACTS devices. Two types of FACTS devices, thyristor controlled series compensators (TCSC) and Static VAR Compensator (SVC) are considered in this method. The basic bacterial foraging algorithm (BFA) is an evolutionary optimization technique inspired by the foraging behavior of the E. coli bacteria. The strategy of the OPF problem is decomposed in two sub-problems, the first sub-problem related to active power planning to minimize the fuel cost function, and the second sub-problem designed to make corrections to the voltage deviation and reactive power violation based in an efficient reactive power planning of multi Static VAR Compensator (SVC). The specified power flow control constraints due to the use of FACTS devices are included in the OPF problem. The proposed method decomposes the solution of such modified OPF problem into two sub problems’ iteration. The first sub problem is a power flow control problem and the second sub problem is a modified Bacterial foraging algorithm (MBFA) OPF problem. The two sub problems are solved iteratively until convergence. Case studies are presented to show the effectiveness of the proposed method.

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.

Wintering Swan Geese maximize energy intake through substrate foraging depth when feeding on buried Vallisneria natans tubers

Background: Foraging theory predicts that animals select patches that offer the highest net rate of energy gain. Hence, prey distribution patterns and spatiotemporal heterogeneity play important roles in determining animal feeding patch selection. For waterfowl foraging on buried aquatic plant tubers, the distribution and biomass of these plant organs vary with depth in the substrate. Since excavation costs also increase with depth, the energy intake of the animals foraging on these plants is highly sediment depth dependent. Methods: Here, using observations of Swan Geese (Anser cygnoides) foraging on Vallisneria natans tubers, we test our hypothesis that geese feeding on tubers buried at intermediate sediment depth maximize their daily energy intake because of the interaction between tuber size and abundance with depth. To do this, we measured the distribution patterns of buried Vallisneria tubers under both undisturbed conditions and post-exploitation by geese (i.e. giving-up conditions). We investigated the relationship between tuber size and burial depth, and total tuber biomass within each sediment layer in undisturbed and exploited plots. Finally, we compared modelled Swan Goose daily energy intake feeding on Vallisneria tubers buried at different sediment layers (1–10, 11–20 and 21–30 cm below the surface). Results: Dry weight of Vallisneria tubers linearly increased with burial depth, while average total dry weight density of tubers showed a unimodal relationship, peaking at intermediate levels. Not surprisingly, Swan Geese foraged most intensively on tubers buried at intermediate sediment depths, where they maximize their daily energy intake. Our results support our hypothesis that Swan Geese feeding on tubers at intermediate depths maximize their daily energy intake. Conclusions: Our study is the first to quantify foraging strategies of Swan Geese during the wintering period, emphasizing the importance of plant traits on foraging selection of belowground foragers.

Growth Dynamics and Optimal Harvesting Stage of Two Forage Maize Varieties

SAUMZ 1 and SAUMZ 2 are two forage maize varieties reproduced by distant hybridization.In a randomized block experiment,their fresh forage yield and dry matter yield were investigated on the basis of growth period,leaf stage,and effective accumulated temperature.The nutritional components were measured in a near infrared diffuse reflectance spectroscopy.Referring to the concept of forage grading index,a formula was constructed to calculate the total digestible energy.The investigated characters and indexes were adapted to suitable models of growth dynamics.The results showed that these two varieties have strong heterosis in fresh forage yield,dry matter yield,and total digestible energy.Their growth dynamics were adapted to Logistic models described by general equation y=k/（1＋ae-bx）.The forage grading indexes of these two varieties were decreased linearly along with the increasing of their leaf stage.The stopping point of fast growing period of total digestible energy estimated on the basis of leaf stage was suggested to be used as optimal harvesting stage of forage maize.The optimal harvesting stage was estimated to be tasseling stage for SAUMZ 1 and 19.50-leaf stage for SAUMZ 2.

Advances in Manta Ray Foraging Optimization:A Comprehensive Survey

This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic fields.Introduced in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays’unique foraging behaviors—specifically cyclone,chain,and somersault foraging.These biologically inspired strategies allow for effective solutions to intricate physical challenges.With its potent exploitation and exploration capabilities,MRFO has emerged as a promising solution for complex optimization problems.Its utility and benefits have found traction in numerous academic sectors.Since its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor&Francis,as well as at international conference proceedings.This paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization challenges.Research trends indicate that 12%,31%,8%,and 49%of MRFO studies are distributed across these four categories respectively.

Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good performance.On the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets.In this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously.The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets.Additionally,it is applied to a disease Covid-19 dataset.The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

A novel power system reconfiguration for a distribution system with minimum load balancing index using bacterial foraging optimization algorithm

In this paper, the objective of minimum load balancing index （LBI） for the 16-bus distribution system is achieved using bacterial foraging optimization algorithm （BFOA）. The feeder reconfiguration problem is formulated as a non-linear optimization problem and the optimal solution is obtained using BFOA. With the proposed reconfiguration method, the radial structure of the distribution system is retained and the burden on the optimization technique is reduced. Test results are presented for the 16-bus sample network, the proposed reconfiguration method has effectively decreased the LBI, and the BFOA technique is efficient in searching for the optimal solution.

What elk, wolves and caterpillars have in common—The perfect forager theorem

It is widely accepted that the Marginal Value Theorem (MVT) describes optimal foraging strategies of animals and the mechanism proposed by the MVT has been supported by a number of field observations. However, findings of many researchers indicate that in natural conditions foragers do not always behave according to the MVT. To address this inconsistency, in a series of computer simulation experiments, we examined the behaviour of four types of foragers having specific foraging efficiencies and using the MVT strategies in 15 different landscapes in an ideal environment (no intra-and inter-specific interactions). We used data on elk (Cervus elaphus) to construct our virtual forager. Contrary to the widely accepted understanding of the MVT (residence time in a patch should be longer in environments where travel time between patches is longer) we found that in environments with the same average patch quality and varying average travel times between patches, patch residence times of some foragers are not affected by travel times. Based on our analysis we propose a mechanism responsible for this observation and formulate the perfect forager theorem (PFT). We also introduce the concepts of a foraging coefficient (F) and foragers’ hub (α), and propose a model to describe the relationship between the perfect forager and all other forager types.

A new dynamic bacterial foraging optimization and its application on model reduction

Inspired by the foraging behavior of E.coli bacteria,bacterial foraging optimization(BFO)has emerged as a powerful technique for solving optimization problems.However,BFO shows poor performance on complex and high-dimensional optimization problems.In order to improve the performance of BFO,a new dynamic bacterial foraging optimization based on clonal selection(DBFO-CS)is proposed.Instead of fixed step size in the chemotaxis operator,a new piecewise strategy adjusts the step size dynamically by regulatory factor in order to balance between exploration and exploitation during optimization process,which can improve convergence speed.Furthermore,reproduction operator based on clonal selection can add excellent genes to bacterial populations in order to improve bacterial natural selection and help good individuals to be protected,which can enhance convergence precision.Then,a set of benchmark functions have been used to test the proposed algorithm.The results show that DBFO-CS offers significant improvements than BFO on convergence,accuracy and robustness.A complex optimization problem of model reduction on stable and unstable linear systems based on DBFO-CS is presented.Results show that the proposed algorithm can efficiently approximate the systems.

Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network

A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data.Thus,in this paper,a Radial Basis Function Neural Network(RBFNN)trained using Manta Ray Foraging Optimization algorithm(MRFO)is proposed.RBFNN is a three-layer network comprising of input,output,and hidden layers that can process large amounts.The trained network can discover hidden data patterns in unseen data.The learning algorithm and seed selection play a vital role in the performance of the network.The seed selection is done using the spectral indices to further improve the performance of the network.The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays.It emulates three unique foraging behaviours namelys chain,cyclone,and somersault foraging.The satellite images contain enormous amount of data and thus require exploration in large search space.The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively.The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager(OLI)images of New Brunswick area.The method was applied to identify and classify the land cover changes in the area induced by flooding.The images are classified using the proposed method and a change map is developed using post classification comparison.The change map shows that a large amount of agricultural area was washed away due to flooding.The measurement of the affected area in square kilometres is also performed for mitigation activities.The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased.The performance of the proposed method is done with existing state-of-the-art methods.

An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition

The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer interfaces.Moreover,sign language recognition is mainly developed for enabling communication between deaf and dumb people.In conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture recognition.Still,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification outputs.Hence,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques.During image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion.Then,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness value.Moreover,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate.Finally,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition output.During results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.

Comparison of particle swarm and bacterial foraging optimization algorithms for therapy planning in HIV/AIDS patients

In HIV/AIDS patients, antiretroviral therapy （ART） is used for reducing the viral load and helps in increasing the life span of the individual. However, severe side effects are associated with the use of antiretroviral drugs. Hence, a treatment schedule, using minimal amount of drugs, is required for maintaining a low viral load and a healthy immune system. The objective of this work is to compute the optimal dosage of antiretroviral drugs for therapy planning in HIV/AIDS patients, using intelligent optimization techniques. In this work, two computational swarm intelligence techniques known as the particle swarm optimization （PSO） and bacterial foraging optimization （BFO） in conjunction with the three-dimensional mathematical model of HIV/AIDS have been used for estimating the optimal drug dosage for administering therapy by minimization of viral load as well as the total drug concentration. Results demonstrate that, using the proposed method, it is possible to achieve minimal viral load and an improved immune system, with the estimated drug dosage. Further, it was observed that the efficiency of BFO （CD4 cells = 757 cells/mm^3 at seventh year of infection） for estimation of optimal drug dosage is higher than the PSO method （CD4 cells = 817 cells/mm^3 at seventh year of infection）. This work seems to be of high clini- cal relevance since, at present, ART is the widely used procedure for treatment of HIV infected patients.

基于最优觅食算法的增材制造中多种类零件分批排样研究

增材制造中多种类零件分批排样存在打印时间成本高和工作台空间利用率低等问题,且需考虑零件高度的影响。分批排样问题包括零件在工作台上放置策略和成型批次分配两个子问题。放置策略涉及成型方向选择、零件碰撞检测和定位策略。基于建立的成型方向准则,利用多边形表示零件投影轮廓,提出基于临界多边形的改进移动碰撞法以确定3种不同形态多边形零件的免碰撞排放范围,提出新的左下定位策略放置零件,新定位策略融合了改进的建设性方法和契合度;提出单机台面积占用最大化策略实现零件成型批次分配。基于上述研究,提出基于最优觅食算法的分批排样算法,算法采用双重编码表达零件放置顺序和旋转角度,以最小化完工时间为目标实现优化分批排样。以案例库零件为对象,与3种对比算法比较,表明所提方法的方案能有效提高增材制造的空间利用率和缩短完工时间。