Honey Bee Mating Research Articles

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.

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.

Observation of Genetic Gain with Instrumental Insemination of Honeybee Queens

Controlling mating in the honeybee (Apis mellifera) is part of one of the greatest challenges for the beekeeping industry's genetic selection programs due to specific characteristics of their reproduction. Several techniques for supervising honeybee mating with relative effective control have been developed over the years to allow honeybee selection. As part of this project, we compared the genetic gains for several colony performance traits, obtained using the BLUP-animal method, according to the selection pressure applied in controlled reproduction (directed fertilization versus instrumental insemination). Our results show similar genetic gains for hygienic behavior and honey production between colonies whether queens were fertilized naturally or via instrumental insemination, as well as similar or lower genetic gains for colonies with queens inseminated for spring development. In addition, we noticed greater fragility in queens following insemination. These findings show that instrumental insemination is an effective tool for reproductive control in genetic selection and for estimating breeding values more precisely. However, this technique does not result in queens of superior genetic quality for commercial purposes.

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.

Stationary and mobile storages-based renewable off-grid system planning considering storage degradation cost based on information-gap decision theory optimization

This paper presents the planning of a hybrid renewable system with wind turbines and bio-waste energy units along with stationary (i.e., batteries) and mobile (i.e., electric vehicles) energy storage. This model minimizes the cost of construction, maintenance and storage degradation. In this model, the energy supply priority is given to renewable sources. Then the mentioned storage devices are used to cover the gap between the load profile and the renewable generation power. The proposed scheme addresses the uncertainties of loads, renewable power and energy consumption of mobile storage devices. Therefore, robust optimization based on information-gap decision theory (IGDT) is utilized to obtain a robust solution against the prediction error of the aforementioned uncertainties. Also, the hybrid optimization algorithm of honey bee mating and artificial bee colony is derived, showing the optimal solution with low dispersion in the final response. The proposed design is applied to the data of the city of Espoo, Finland. The obtained numerical results show the capability of the proposed design in deriving robust economic planning for the proposed hybrid system. So, the used hybrid solution algorithm is able to obtain the best solution in a lower computation time compared to non-hybrid solvers, and it has a low standard deviation around 0.94 % in the final response. The presence of the aforementioned renewable sources leads to attaining an environment-friendly hybrid system. The optimal performance of the storage devices leads to the robustness of the optimal solution against the maximum error of 18.19 % in the prediction of uncertainties. This occurs considering the smart charging management of mobile storage, but the maximum uncertainty level of 11.12 % holds to non-smart management of these storage devices in the proposed scheme. The energy management of mobile storage devices based on smart (non-smart) charging strategy also reduces (increases) the planning cost of the off-grid system by 7.62 % (39.68 %) compared to their absence.

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.

Honeybees mating system and the functionality

Honeybees mating system and the functionality

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.

Optimal Intelligence Planning of Wind Power Plants and Power System Storage Devices in Power Station Unit Commitment Based

Renewable energy sources (RES) such as wind turbines (WT) and solar cells have attracted the attention of power system operators and users alike, thanks to their lack of environmental pollution, independence of fossil fuels, and meager marginal costs. With the introduction of RES, challenges have faced the unit commitment (UC) problem as a traditional power system optimization problem aiming to minimize total costs by optimally determining units’ inputs and outputs, and specifying the optimal generation of each unit. The output power of RES such as WT and solar cells depends on natural factors such as wind speed and solar irradiation that are riddled with uncertainty. As a result, the UC problem in the presence of RES faces uncertainties. The grid consumed load is not always equal to and is randomly different from the predicted values, which also contributes to uncertainty in solving the aforementioned problem. The current study proposes a novel two-stage optimization model with load and wind farm power generation uncertainties for the security-constrained UC to overcome this problem. The new model is adopted to solve the wind-generated power uncertainty, and energy storage systems (ESSs) are included in the problem for further management. The problem is written as an uncertain optimization model which are the stochastic nature with security-constrains which included undispatchable power resources and storage units. To solve the UC programming model, a hybrid honey bee mating and bacterial foraging algorithm is employed to reduce problem complexity and achieve optimal results.

Optimal IoT-based decision-making of smart grid dispatchable generation units using blockchain technology considering high uncertainty of system

This article proposes a blockchain-based architecture for the optimal scheduling of the dispatchable units in the smart grids considering the high penetration of renewable energy sources. The proposed method makes use of blockchain technology to secure the data exchange among the agents in the smart grid and avoid unauthorised access to the real data of the system. Considering the flexibility which is provided by the optimal switching in the smart grid, the reconfiguration problem is also considered in the model using some remote control switches. A stochastic framework based on the unscented transform is proposed to not only capture the independent randomness of the renewables but also to handle the correlated uncertainties among them. The problem formulation is constructed as a multi-objective optimization problem optimizing the total operating cost and the reliability indices. Due to the nonlinear and complex nature of the problem, a new optimization method based on the honey bee mating (HBM) algorithm is proposed to solve the problem without any simplification or linearization. Moreover, a correction approach based on local search is proposed to increase the algorithm capability. The appropriate performance of the proposed model is examined on a test smart grid. The data for the optimal decision is based on the internet of things (IoT) data.

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.

Microstrip leaky wave antenna for wide range of beam scanning in X band

AbstractIn this work, design of a pattern reconfigurable antenna is presented for X‐band applications as a leaky wave antenna (LWA) realization. Due to the advantages of simple design schematic, low profile, and easy impedance matching, LWA designs have been using as an efficient solution for reconfigurable antenna in many wireless communication applications. Optimization and simulation of the proposed LWA is carried out in 3‐D Microwave simulation CST environment and Honey Bee Mating is used as the optimization algorithm. Dimensions of the proposed LWA is 130 × 30 mm, which is fabricated on Rogers 5880 as capacitive loaded eight identical rectangular microstrip patches operating over 9.5‐12 GHz with a fractional bandwidth. The proposed LWA exhibits a measured peak gain of 11.8 dBi at 10 GHz with an overall gain characteristic of between 9.5 and 11.8 dBi over the range of 9.5 to 12 GHz with a steerable pattern characteristics between −45 and 50° as agreed with the theory. Furthermore, the fabricated LWA has been found to have the superior performance as compared with its counterparts.

The Italian National Beekeeping Registry (BDNA) as a Tool to Identify Areas Suitable for Controlled Mating of Honey Bees in Italy

Honey bee mating is difficult to control as it occurs in flight, several kilometers away from the hive of origin of both queens and drones. In recent years, there has been an increase in the introduction into Italy of queen bees and swarms originating from interracial crossings (especially those known as “Buckfast”), or belonging to non-native subspecies. Consequently, there is a strong need to establish areas suitable for controlled mating. The Italian national beekeeping registry (BDNA) has been recently set up to monitor the position of apiaries and movement of hives, with the purpose of economic and sanitary protection and improvement of bee heritage and food safety. The BDNA was, thus, deemed a suitable tool to explore the availability of areas that could be used as mating stations. Specific criteria, such as not be classified as wetlands, altitude below 1500 m a.s.l., distance of at least 6000 m from an apiary, and distance of at least 1500 m from the national terrestrial border, were selected for defining the mating stations. Furthermore, all the remaining Italian national territory was considered suitable for settlement of mating stations, including towns, industrial settlements, and roads. Based on these criteria, the following methodological framework was applied: (1) collection of the required data (i.e., apiaries coordinates, geospatial data on soil classification, geospatial data on terrain altitude, Italian boundary geospatial data); (2) calculation for the excluded areas according with the criteria listed above; (3) calculation of the mating areas by means of the difference between the entire Italian territory and the excluded areas. The database of BDNA was used to identify them. Appropriate databases and software were used to exclude the areas of the Italian national territory that did not fulfill the established criteria. In this paper, we report the procedure applied to identify on maps the areas suitable for controlled mating of queen bees in the Italian national territory based on data currently available in the BDNA.

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.

A theoretical derivation of response to selection with and without controlled mating in honeybees

BackgroundIn recent years, the breeding of honeybees has gained significant scientific interest, and numerous theoretical and practical improvements have been made regarding the collection and processing of their performance data. It is now known that the selection of high-quality drone material is crucial for mid to long-term breeding success. However, there has been no conclusive mathematical theory to explain these findings.MethodsWe derived mathematical formulas to describe the response to selection of a breeding population and an unselected passive population of honeybees that benefits indirectly from genetic improvement in the breeding population via migration. This was done under the assumption of either controlled or uncontrolled mating of queens in the breeding population.ResultsOur model equations confirm what has been observed in simulation studies. In particular, we have proven that the breeding population and the passive population will show parallel genetic gain after some years and we were able to assess the responses to selection for different breeding strategies. Thus, we confirmed the crucial importance of controlled mating for successful honeybee breeding. When compared with data from simulation studies, the derived formulas showed high coefficients of determination > 0.95 in cases where many passive queens had dams from the breeding population. For self-sufficient passive populations, the coefficients of determination were lower (sim 0.8) if the breeding population was under controlled mating. This can be explained by the limited simulated time-frame and lower convergence rates.ConclusionThe presented theoretical derivations allow extrapolation of honeybee-specific simulation results for breeding programs to a wide range of population parameters. Furthermore, they provide general insights into the genetic dynamics of interdependent populations, not only for honeybees but also in a broader context.

A novel fuzzy cloud stochastic framework for energy management of renewable microgrids based on maximum deployment of electric vehicles

The high penetration of renewable energy sources in varied types along with the electric vehicles charging demand has created new challenges against the optimal operation and management of these systems. Therefore, this article attempts to formulate, model and operate the renewable microgrids considering different types of renewable sources including solar units, wind units and storages in the presence of electric vehicles. Due to the volatile and random nature of renewable sources and the charging demands by the electric vehicles, it is needed to find a solution for optimal control of these technologies. In this way, a new policy is devised for control of the charging demand in three different schemes, so-called the smart scheme, coordinated scheme and uncoordinated scheme. In order to capture the influence of uncertainties rooting from the renewable sources in the problem, a novel intelligent approach based on fuzzy cloud theory is used which generates an entropy-entropy concept for its task. Through this model, not only the uncertainty of the random variables is handled, but also the uncertainty of the probability density function (PDF) type is also deployed. In order to solve the problem, a swarm optimization based on honeybee mating algorithm (HMA) is proposed which is equipped by the polar search operators for making a symmetrical searching frame. The simulation results on a typical renewable microgrid test system advocate the quality and appropriate efficacy of the model.

The neuroethology of olfactory sex communication in the honeybee Apis mellifera L.

The honeybee Apis mellifera L. is a crucial pollinator as well as a prominent scientific model organism, in particular for the neurobiological study of olfactory perception, learning, and memory. A wealth of information is indeed available about how the worker bee brain detects, processes, and learns about odorants. Comparatively, olfaction in males (the drones) and queens has received less attention, although they engage in a fascinating mating behavior that strongly relies on olfaction. Here, we present our current understanding of the molecules, cells, and circuits underlying bees' sexual communication. Mating in honeybees takes place at so-called drone congregation areas and places high in the air where thousands of drones gather and mate in dozens with virgin queens. One major queen-produced olfactory signal-9-ODA, the major component of the queen pheromone-has been known for decades to attract the drones. Since then, some of the neural pathways responsible for the processing of this pheromone have been unraveled. However, olfactory receptor expression as well as brain neuroanatomical data point to the existence of three additional major pathways in the drone brain, hinting at the existence of 4 major odorant cues involved in honeybee mating. We discuss current evidence about additional not only queen- but also drone-produced pheromonal signals possibly involved in bees' sexual behavior. We also examine data revealing recent evolutionary changes in drone's olfactory system in the Apis genus. Lastly, we present promising research avenues for progressing in our understanding of the neural basis of bees mating behavior.