Honey Bee Mating Optimization Research Articles

A Simulated-Annealing-Quasi-Oppositional-Teaching-Learning-Based Optimization Algorithm for Distributed Generation Allocation

Conventional evolutionary optimization techniques often struggle with finding global optima, getting stuck in local optima instead, and can be sensitive to initial conditions and parameter settings. Efficient Distributed Generation (DG) allocation in distribution systems hinges on streamlined optimization algorithms that handle complex energy operations, support real-time decisions, adapt to dynamics, and improve system performance, considering cost and power quality. This paper proposes the Simulated-Annealing-Quasi-Oppositional-Teaching-Learning-Based Optimization Algorithm to efficiently allocate DGs within a distribution test system. The study focuses on wind turbines, photovoltaic units, and fuel cells as prominent DG due to their growing usage trends. The optimization goals include minimizing voltage losses, reducing costs, and mitigating greenhouse gas emissions in the distribution system. The proposed algorithm is implemented and evaluated on the IEEE 70-bus test system, with a comparative analysis conducted against other evolutionary methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Honey Bee Mating Optimization (HBMO), and Teaching-Learning-Based Optimization (TLBO) algorithms. Results indicate that the proposed algorithm is effective in allocating the DGs. Statistical testing confirms significant results (probability < 0.1), indicating superior optimization capabilities for this specific problem. Crucially, the proposed algorithm excels in both accuracy and computational speed compared to other methods studied.

Economic dispatch optimization considering operation cost and environmental constraints using the HBMO method

Increasing electrical energy use has led to the development of power systems so nowadays some power systems have been expanded into geographical regions whose total size is equal to a continent. In line with this development, which has several benefits, like other fields, it has been discussed new issues in the power system field such as economic distribution. Over several decades and with the growing consumption of electrical energy, its supply systems have increased so that today the load distribution among energy production units with the lowest cost has become one of the most widely and complicated issues of power system utilization. In this paper, a mutant version of the honey bee mating optimization (HBMO) algorithm based on collective intelligence is proposed for power system economic-emission dispatch (E-ED). The proposed algorithm has been used to solve the E-ED problem with nonlinear cost functions including plant-induced limitations such as steam inlet valves, balancing the production and consumption in the system, restricted zones, production limits, and increasing and decreasing rates. Moreover, considering production cost functions, environmental pollution, and losses, the load distribution issue, which is one of the most important issues in today’s system, has been investigated. In this paper, we attempt to increase the efficiency and balance of the standard algorithm for local and final searches using the adaptive nonlinear system. In the proposed algorithm, the final optimized answer is considered a criterion to be stopped the program optimization. In addition, to improve the search for the final answer, improvements have been made in the local and final search structure. The simulation results show that the efficiency of this algorithm in solving the problem of economic load distribution is better than other algorithms.

A novel optimal management method for smart grids incorporating cloud-fog layer and honeybee mating optimization algorithm

As a result of cloud-fog (CF) technology, today's society is experiencing low delay, great performance, improved safety, and lower operating costs. As a result of integrating Smart Grid (SG) into the CF platform, the power supply is of higher quality, and the transmission, distribution, and production of power are secure; management of demand and supply is continuous. The study proposes integrating SG with CF-driven environments to improve source distribution. There are 6 fogs in various areas. Every fog connects to a cluster, and there are 500 smart houses in every cluster. Home energy demands are fulfilled by fogs using various load balancing algorithms (LBAs), which are implemented on Digital Twin Virtual Machines (DTDTVMs) and provided with a fast response time (RT) and processing time (PT). The study proposes a Honeybee Mating Optimization Algorithm to balance loads using improved service broker policies. After comparing the outcomes of the suggested LBA to the particle swarm optimization algorithm (PSO), it can be concluded the suggested LBA performs better in comparison with PSO. All the simulation results and comparisons have been performed on digital twin environment to provide more realistic framework for this study.

Stochastic optimization – based economic design for a hybrid sustainable system of wind turbine, combined heat, and power generation, and electric and thermal storages considering uncertainty: A case study of Espoo, Finland

This work-study deals with the economic planning of an island sustainable system that consists of a wind turbine, battery, combined heat and power system, and thermal storage to meet electrical and thermal loads at the same time. The approach attempts to find minimum capital cost, repair and maintenance cost, and operating cost, as well as minimize the pollution of these elements. It is also constrained by the operation-planning model of the mentioned resources and storage, provided that a wind turbine and combined heat and power system have priority in electrical load supply, a combined system of heat and power has priority in thermal load supply, and batteries and storage devices have the task of filling the gap between supply and demand profiles. Uncertainty parameters of the scheme include electrical and thermal load and power output of the wind system. The scheme adopts the unscented transformation method to provide proper modeling of the mentioned uncertainties. Moreover, the essence of the problem is integer nonlinear programming, which uses the combined gray wolf optimization and honey bee mating optimization algorithm to achieve the desired solution. Simultaneous supply of electrical and thermal loads, modeling of uncertainties, and adoption of a combined solver in the proposed scheme are among the contributions. Finally, by implementing this scheme on data from the city of Espoo, Finland, the numerical results indicate a reduction of 10%–11% in the planning cost of the proposed scheme compared to a design that only supplies electricity.

Performance Augmentation of Cuckoo Search Optimization Technique Using Vector Quantization in Image Compression

For constructing the best local codebook for image compression, there are many Vector Quantization (VQ) procedures, but the simplest VQ procedure is the Linde–Buzo–Gray (LBG) procedure. Techniques such as the Gaussian Dissemination Function (GDF) are used for the searching process in generating a global codebook for particle swarm optimization (PSO), Honeybee mating optimization (HBMO), and Firefly (FA) procedures. However, when particle velocity is very high, FA encounters a problem when brighter fireflies are trivial, and PSO suffers uncertainty in merging. A novel procedure, Cuckoo Search–Kekre Fast Codebook Generation (CS-KFCG), is proposed that enhances Cuckoo Search–Linde–Buzo–Gray (CS-LBG) codebook by implementing a Flight Dissemination Function (FDF), which produces more speed than other states of the art algorithms with appropriate mutation expectations for the overall codebook. Also, CS-KFGC has generated a high Peak Signal Noise Ratio (PSNR) in terms of high duration (time) and better acceptability rate.

Integrated Optimization of Process Planning and Scheduling for Aerospace Complex Component Based on Honey-Bee Mating Algorithm

To cope with the problems of poor matching between processing characteristics and manufacturing resources, low production efficiency, and the hard-to-meet dynamic and changeable model requirements in multi-variety and small batch aerospace enterprises, an integrated optimization method of complex component process planning and workshop scheduling for aerospace manufacturing enterprises is proposed. This paper considers the process flexibility of aerospace complex components comprehensively, and an integrated optimization model for the process planning and production scheduling of aerospace complex components is established with the optimization objectives of achieving a minimum makespan, machining time and machining cost. A honey-bee mating optimization algorithm (HBMO) combined with the greedy algorithm was proposed to solve the model. Then, it formulated a four-layer encoding method based on a feature-processing sequence, processing method, and machine tool, a tool was designed, and five worker bee cultivation strategies were designed to effectively solve the problems of infeasible solutions and local optimization when a queen bee mated to a drone. Finally, taking the complex component parts of an aerospace enterprise as an example, the integrated optimization of process planning and workshop scheduling is carried out. The results demonstrate that the proposed model and algorithm can effectively shorten the makespan and machining time, and reduce the machining cost.

A Multiobjective Evolutionary Approach for Solving the Multi-Area Dynamic Economic Emission Dispatch Problem Considering Reliability Concerns

Economic dispatch (ED) problems, especially in multi-area power networks, have been challenging concerns for power system operators for several decades. In this paper, we introduce a novel approach for solving the multiobjective multi-area dynamic ED (MADED) problem in the presence of practical constraints such as valve-point effect (VPE), prohibited operating zone (POZ), multi-fuel operation (MFO), and ramp rate (RR) limitations. Different objective functions including energy not supplied (ENS), generation costs, and emissions are investigated. The reliability objective, which has been less studied in economic dispatch area, distinguishes the proposed study from other studies. A compromise has been made from economic and reliability points of view. The MADED problem in the power system is inherently a complex and nonlinear problem, considering the operational constraint increments and the intricacy of the problem. Hence, the modified grasshopper optimization (MGO) algorithm based on a chaos mechanism is presented to prevent being trapped in local optima. The proposed method is tested on two systems including a 10 unit, 3-zone test system and a 40-unit 3-zone test system, and then, the outcomes are compared with those of other evolutionary techniques such as gray wolf optimization (GWO) and modified honey bee mating optimization (MHBMO). The simulation results demonstrate that the suggested strategy is successful in resolving both single-objective and multiobjective MADED problems.

Modelling and Analysis of the Cone Coupling Problem Using Optimization

A coupling is a mechanism that transmits operative power between two shafts that are revolving at different speeds. A coupling connects two shafts at their ends and can slip or fail depending on the torque limit. It is an essential component of any power transmission system and may survive for a very long time if properly designed and maintained. This study's current research, a newly developed optimization algorithms are used to minimize the volume of cone coupling. The current study presented here compares modern metaheuristic methods for optimizing the design of the cone coupling problem. The algorithms used are particle swarm optimization (PSO), crow search algorithm (CSA), enhanced honeybee mating optimization (EHBMO), Harmony search algorithm (HSA), Krill heard algorithm (KHA), Pattern search algorithm (PSA), Charged system search algorithm (CSSA), Salp swarm algorithm (SSA), Big bang big crunch optimization (B-BBBCO), Gradient based Algorithm (GBA). The performance of these algorithms is assessed both statistically and subjectively. The algorithms' performance is evaluated quantitatively and qualitatively using consistency, simplicity and quality. The experimental results on the cone clutch problem shows that PSO produces greater results than EHBMO, whereas CSA and BBCO produce approximately identical results.

Heart Disease Prediction Model Using Varied Classifiers with Score-Level Fusion

This paper aims to introduce a novel heart disease prediction model. Originally, the input data is subjected for preprocessing, in which the data cleaning takes place. The features like statistical, higher order statistical features, and symmetrical uncertainty are extracted from the preprocessed data. Then, the selected features are subjected to the classification process with an ensemble model that combines the classifiers like deep belief network (DBN), random forest (RF), and neural network (NN). At last, the score level fusion is carried out to provide the final output. To make the classification more precise and accurate, it is intended to tune the weights of DBN more optimally. A new self-adaptive honey bee mating optimization (SAHBMO) algorithm is implemented in this work for this optimal tuning. Finally, the performance of the presented scheme is computed over the existing approaches in terms of different metrics.

Dynamic Rule Curves and Streamflow under Climate Change for Multipurpose Reservoir Operation Using Honey-Bee Mating Optimization

Climate change in the watershed above the reservoir has a direct impact on the quantity of streamflow that enters the reservoir and the management of water resources. Developing effective reservoir rule curves helps reduce the risk of future failures of water resource management. The purpose of this study was to analyze the influence of climate change on the volume of streamflow entering the Ubolratana Reservoir, Thailand during the years 2020–2049 with climate simulations from the CIMP5 model under RCP4.5 and RCP8.5 scenarios. SWAT models were used to forecast future reservoir streamflow quantities. Moreover, suitable reservoir rule curves using the Honey-Bee Mating Optimization (HBMO) were developed and the effectiveness of the new rule curves was assessed. According to the research findings, the average yearly streamflow in the future apparently grew from 32% in the base years (2011–2019) and 65% under the RCP4.5 and RCP8.5 scenarios, respectively. It was discovered that the average monthly streamflow was higher in the rainy season than in the dry season. Both of the projected situations have a form compatible with the present rule curves in the section of the new reservoir rule curves generated with the HBMO. Furthermore, the newly constructed rule curves may allow the reservoir to keep more water during the rainy season, thereby assuring that there will be adequate water during the following dry season. Additionally, during the dry season, the reservoir was able to release more water that would be able to reduce the water shortage, indicating that it was able to effectively reduce the amount of water shortage and average overflow under RCP4.5 and RCP8.5 situations.

Evaluation and optimization of engine performance and exhaust emissions of a diesel engine fueled with diestrol blends

AbstractBiofuel is a nontoxic and renewable fuel and could be used in diesel engines in combination with diesel to reduce emissions. The effects of various biodiesel–ethanol–diesel (BED) blends were investigated on the engine power, specific fuel consumption (SFC), engine vibration, and pollutant emissions. The engine experiments were performed using nine castor biodiesel and ethanol blends under five different engine speeds. The engine power was not influenced by different BED blends while B5E4, and B5E6 fuel blends had the highest SFC. The engine vibration slightly increased when fueled with BED blends. Hydrocarbon emission diminished by increasing biodiesel percentage in the blends, whereas it increased by substituting more ethanol in BED blends. NOx emission was increased by adding more biodiesel into BED blends, while ethanol performed vice versa. The results showed that CO emission was enhanced by increasing ethanol percentage, while it decreased with more biodiesel addition. There was not a remarkable difference in CO2 emissions between different treatments. A novel combination of an Adaptive Neuro‐Fuzzy Inference System (ANFIS) modeling and Honeybee Mating Optimization (HBMO) method was adopted to find a global optimum BED blend considering various output parameters. The selected ANFIS model satisfactorily predicted the engine efficiency and exhaust emissions with R2, mean absolute percentage error (MAPE), and root‐mean‐squared error (RMSE) of 0.99, 0.02, and 0.11, respectively. Consequently, using the HBMO algorithm, the BED blend with 15% of biodiesel, 6% of ethanol, and 79% of pure diesel was found to be the most optimal BED blend for a diesel engine at an engine speed of 950 rpm.

Fruit Image Segmentation Using Teacher-Learner Optimization Algorithm and Fuzzy Entropy

To perform robust fruit image segmentation is a challenging task due to several variances in the varieties. Thresholding is one of the popular segmentation methods in recent times. The fuzzy entropy scheme has been mostly applied to image thresholding. Fuzzy membership functions are the major source for segmenting the image by fuzzy entropy and thresholding. In the proposed paper, the Teacher-Learner-Based Optimization (TLBO) algorithm is involved in searching for optimal threshold values combination for fruit image segmentation based on fuzzy entropy. The proposed scheme is performed on red apple, green apple, golden apple, guava, and orange fruit images. For comparison of segmentation results of the proposed scheme with existing approaches, PSNR and uniformity parameters are utilized. It has been observed that the performance of the proposed approach is more effective than GA (Genetic Algorithm), BFO (Bacteria Foraging Optimization), and HBMO (Honey Bee Mating Optimization) approaches.

A Review on Evolutionary Methodology more appropriate for implementation in Economic Load Dispatch

Economic Load Dispatch (ELD) is approach to reduced the cost of electricity generation without any compromisation on its quality and its reliability. As per the economic point of view the production cost must be minimized. For the different load demands at different time period vary as well as requirement of power also vary. For this it is very important to monitorized the demand and its production. The principle factor in Thermal and Nuclear power plant is Fuel cost. In this paper we have studied different evolutionary method and obtain the best method among them perhaps, in order to bring the best solution to ELD problems an evolution of soft computing methods and many other methods have been successfully conducted such as Simulated Annealing (SA), Genetic Algorithm (GA), Honey Bee Mating Optimization (HBMO), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA) and PSO-GSA hybrid method. To bring more appropriate optimized solution for ELD problems.

Design Optimization of Ultra-Wideband Vivaldi Antenna using Artificial Intelligence

In this work, computationally efficient design optimization of frequency selective surface (FSS)-loaded ultra-wideband Vivaldi antenna via the use of data-driven surrogate model is studied. The proposed design methodology consists of a multi-layer FSS structure aimed for performance improvement of the Vivaldi design, which makes the design a multi-objective multi-dimensional optimization problem. For having a fast and accurate optimization process, a data-driven surrogate model alongside the metaheuristic optimizer honeybee mating optimization (HBMO) had been used. The optimally designed antenna had been prototyped and its performance characteristics had been measured. The obtained experimental results are compared with the simulated results of the proposed method. Results show that the obtained FSS-loaded structure has enhanced directivity compared with the design without FSS structure, without any performance losses in the return loss characteristics. The FSS-loaded Vivaldi antenna operates at 2–12 GHz band with a maximum gain of 10 dBi at 10 GHz which makes the design a good solution for RADAR applications.

3D EM data‐driven artificial network‐based design optimization of circular reflectarray antenna with semi‐elliptic rings for X‐band applications

AbstractIn this work, the design and realization of a circular reflectarray antenna (CERA) with semi‐ elliptic rings are carried out for X‐band. In order to achieve a fast design optimization process, 3D EM simulated data‐driven multilayer perceptron neural network (MLP NN) model of the unit element is generated providing the unit reflection phase as a continuous function of the radii and gap of the ring for the width w = 0.5 mm on the FR4 with ∊r = 4.4, h = 1.5 mm. The proposed CERA consists of 148 unit cells taken symmetrical both in the Y and X‐axis. Thus, a meta‐heuristic optimization algorithm honey bee mating optimization (HBMO) is applied to 21 elements between 10 and 12 GHz to determine their optimal geometries for the required reflection angles according to the phasing scheme. The simulated and measured performances of the designed CERA have been found in good agreement and its performance is compared also with its counterpart designs in literature and resulted to exhibit superior performance.

Distributed energy resource placement considering hosting capacity by combining teaching–learning-based and honey-bee-mating optimisation algorithms

An emerging challenge of most electric utilities is to expand hosting capacity constraints and maximise the interconnection of distributed energy resources (DER). DERs have proliferated in distribution systems in most jurisdictions, and this proliferation has not occurred in an orderly fashion. Reports of degradation in power quality and operations are common. With the continuing adoption of DERs, it is imperative to develop methods to unlock the potential of DERs in a way that they are used to support system needs, rather than degrading them. This concept’s advantages are varied in different power system scenarios based on the considered models for owning and operating DER. With this objective in mind, this paper proposes a new algorithm to identify the optimal locations for new DERs. The proposed method is a novel combination of teaching–learning-based optimisation (TLBO) and honey-bee-mating optimisation (HBMO) algorithms, and it capitalises on both techniques’ benefits. A major novelty of this method is that it fully accounts for current hosting capacity and all existing DERs and incorporates these parameters in the optimisation algorithm. The results reveal that adding fuzzy clustering to the multiobjective process improves the DER placement problem. The DERs considered in this optimisation problem are fuel cell units, and the objective function includes cost, losses, and voltage deviation. The proposed technique is adopted in the IEEE 70-bus-radial test system, and its performance is compared with those of other optimisation algorithms. The results reveal the proposed algorithm’s superiority in accuracy and computational speed when reaching the optimal solution. As a case in point, the calculation time of the proposed algorithm is about %9 and %22 faster than TLBO and HBMO, respectively.

Optimization of Concrete Gravity Dam Section using New Election Meta-heuristic Algorithm

According to design criteria, site specifications, and required height, different dimensional options are considered for geometric parameters, while design gravity dams. Among options, an option that provides sustainability and low costs is considered as an executive option. In this study, the Koyna concrete gravity dam section in India and the Kalat Zavin concrete gravity dam in Khorasan Razavi province were optimized using the election meta-heuristic algorithm (EA) concerning the stability conditions. In this model, geometric parameters were considered as decision variables and dam weight as an objective function. The model obtains geometric parameters in such a way that the dam weight is minimized. To evaluate the proposed method, the Koyna dam and Kalat Zavin dam data set were used and the results were compared with Genetic (GA), Honey Bee Mating Optimization (HBMO), and Imperialist Competitive (ICA) algorithms. The results of the EA method indicate a 9.87% and 10.40% decrease in the volume of concrete consumption and a reduction in area for Koyna and Kalat Zavin dams. Based on the comparisons, the EA algorithm showed better performance in optimizing the dam section than the genetic algorithm (GA), honey bee mating algorithm (HBMO), and imperialist competitive algorithm (ICA) optimization models. optimization models. Also, the improvement rate of the EA algorithm in comparison with ICA, HBMO, and GA algorithms is equal to 8%, 6%, and 11%, respectively.

Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves

Estimation of discharge flowing through rivers is an important aspect of water resource planning and management. The most common way to address this concern is to develop stage-discharge relationships at various river sections. Various computational techniques have been applied to develop discharge ratings and improve the accuracy of estimated discharges. In this regard, the present study explores the application of the novel hybrid multigene genetic programming-generalized reduced gradient (MGGP-GRG) technique for estimating river discharges for steady as well as unsteady flows. It also compares the MGGP-GRG performance with those of the commonly used optimization techniques. As a result, the rating curves of eight different rivers were developed using the conventional method, evolutionary algorithm (EA), the modified honey bee mating optimization (MHBMO) algorithm, artificial neural network (ANN), MGGP, and the hybrid MGGP-GRG technique. The comparison was conducted on the basis of several widely used performance evaluation criteria. It was observed that no model outperformed others for all datasets and metrics considered, which demonstrates that the best method may be different from one case to another one. Nevertheless, the ranking analysis indicates that the hybrid MGGP-GRG model overall performs the best in developing stage-discharge relationships for both single-value and loop rating curves. For instance, the hybrid MGGP-GRG technique improved sum of square of errors obtained by the conventional method between 4.5% and 99% for six out of eight datasets. Furthermore, EA, the MHBMO algorithm, and artificial intelligence (AI) models (ANN and MGGP) performed satisfactorily in some of the cases, while the idea of combining MGGP with GRG reveals that this hybrid method improved the performance of MGGP in this specific application. Unlike the black box nature of ANN, MGGP offers explicit equations for stream rating curves, which may be counted as one of the advantages of this AI model.

Optimal Phasor Measurement Unit Placement Using a Honey Bee Mating Optimization (HBMO) Technique Considering Measurement Loss and Line Outages

Phasor measurement units (PMUs) are important devices for protection, monitoring, and control of modern power systems. Unlike the supervisory control and data acquisition (SCADA) system which only measure the magnitude, PMUs can provide a set of synchronized phasor measurements with high sampling rate with the accuracy of less than one second, for better system monitoring especially during the outages. Since the PMUs are costly devices, system operator is not able to install a PMU at each bus to maximize the observability of system. To this end, determination of the number of PMUs in the system for having a 100% observability is pivotal. In this paper, honey bee mating optimization (HBMO) approach is utilized to allocate PMUs in modern power systems. Moreover, the impact of line outages, as well as measurement loses, are considered. The simulation results show the effectiveness of the proposed method on solving the PMU placement problem. http://www.crpase.com/xml/20210701211.xml

Internet-of-Things-Assisted Smart Grid Applications in Industry 4.0

This paper studies some recent advances in the field of Smart Grid, including the application of some intelligent algorithms to optimize energy efficiency and energy collection, in order to have some impact on the green theme of Smart Grid under the intelligent system of the Internet of Things. For optimization in SG, evolutionary or heuristic algorithms play an important role. Cat Swarm Optimization (CSO), Emperor Penguin Optimization (EPO), Butterfly Mating Optimazation (BMO), Honey Bee Mating Optimization (HBMO) and many other algorithms have been applied to SG optimization, and more and more flexible, skilled and robust algorithms will be added to contribute to SG optimization in the future.