Honey Bee Mating Optimization Algorithm Research Articles

A deep learning framework for quality control process in the motor oil industry

Given the advancements in the modern world, using Multivariate-Multistage Quality Control (MVMSQC) patterns in continuous production industries is deemed crucial and essential. This study examines the importance and necessity of Multivariate-Multistage Quality Control in manufacturing industries, focusing on motor oil production. Motor oil quality significantly influences engine performance. Thus, the primary objective of this research is to enhance accuracy in detecting faults in quality variables, utilizing deep learning algorithms for visual quality control of data, a facet often overlooked in classical statistical methods and reducing process control time. Combining deep learning algorithms such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for controlling numerical variables, and Residual Networks (ResNet) and Dense Convolutional Networks (DenseNet) for controlling image variables have been employed. Additionally, the Honey Bee Mating Optimization Algorithm (GBC) has been utilized to tune the parameters of the LSTM-CNN and ResNet-DenseNet deep learning algorithms. Combining heuristic algorithms and deep learning algorithms enhances the performance of the final models in quality control processes. A case study in the motor oil production industry is examined to demonstrate the real-world application of the proposed model. The proposed LSTM-CNN hybrid algorithm outperforms individual CNN and LSTM algorithms in fault detection, improving performance by 15% and 8%, respectively. Furthermore, in visual components, the proposed ResNet-DenseNet hybrid algorithm has shown higher accuracy than ResNet and DenseNet algorithms, improving performance by 10% and 15%, respectively. This research significantly contributes to developing Multivariate-Multistage Quality Control in manufacturing industries by employing deep learning methods. The primary aim is to identify faults in quality components and utilize deep learning algorithms for visual quality control of data, a facet often overlooked in classical statistical methods. The research significantly advances multivariate-multistage quality control in manufacturing industries by leveraging deep learning methods. It addresses detecting complex nonlinear patterns in large-scale data with high flexibility and minimal time.

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.

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.

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.

Use of Honey-Bee Mating Optimization Algorithm to Design Water Distribution Network in Gurudeniya Service Zone, Gurudeniya, Sri Lanka

"ENGINEER", established in 1973 by The Institution of Engineers, is a Journal for dissemination of Engineering knowledge, published quarterly.If you are interested in contributing an Original Technical Paper based on research by the author(s) (intended for Section I) or an Original Article of Professional or Technical interest related to Engineering. (Section II) to this journal please go through the publication checklist which can be downloaded from here.Cover DescriptionCover images show views of the Giant tank and the Tekkama anicut.

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.

Placement and Quantitating of FACTS Devices in a Power System Including the Wind Unit to Enhance System Parameters

One of the main concerns of network operators is the enhancement of system parameters; accordingly, a set of different means to this end are posed. However, the use of renewable energies such as the wind could increase the importance of the debate over sustainability and conditions of power system parameters. In this study, the condition of said parameters is examined by placing FACTS (Flexible Alternating Current Transmission System) devices in a 24-bus power system including a wind farm. Research data entailing information on the wind and the amount of consumption load per year are classified by using the K-means classification algorithm; then, the objective function is obtained according to the parameters intended for optimization. This function is optimized by using the Honey-bee mating optimization (HBMO) algorithm followed by obtaining the suitable place and amount for FACTS devices. The results showed that the examined parameters are optimized when using FACTS devices.

Modified honey bees mating optimization algorithm for multi-objective uncertain integrated process planning and scheduling problem

The new technology of intelligent manufacturing makes the data of process planning and shop scheduling easier to interconnect, and the integration optimization of different manufacturing processes is an important technology to ensure the implementation of intelligent manufacturing. Integrated process planning and scheduling is a significant research focus in recent years, which could improve the performance of manufacturing system. At present, the research on integrated process planning and scheduling is insufficient to consider the multi-objective and uncertain characteristics widely existing in real manufacturing environment. Therefore, multi-objective integrated process planning and scheduling problem with uncertain processing time and due date is addressed in this article. The mathematical model of multi-objective uncertain integrated process planning and scheduling problem with uncertain processing time and fuzzy due date is established based on fuzzy set, in which the calculation method of uncertainty measurement objective is designed. An effective modified honey bees mating optimization algorithm has been designed to solve the proposed model. Queens set is constructed to maintain the non-dominated solutions found in the optimization process. The calculation method of mating probability between drone and queen bee based on Euclidean distance is designed. Fuzzy operators were utilized to evaluate fitness, judge the non-dominated relationship, and decode the scheduling solution. Different instances were designed and carried out to test the performance of the proposed method. The results show that the proposed method is very effective for solving multi-objective uncertain integrated process planning and scheduling.

Advanced Honey Bee Coupling Intensification Techniques Combined with Neural Networks for Proficient Facts Connotation

Flock Aptitude Is A Communal Performance Of Societal Classifications Like Individuals Like Ant Cluster Escalation, Fish Training, Birds Assembling, Bee Cluster Optimization And Particle Crowd Escalation. In This Work, A Mixture Crowd Intelligence Based Performance For Statistics Classification Is Suggested Using Honey Bee Mating Optimization Algorithm With Neural Network (HBMO-NN). Honey Bee Reproducing Procedure May Be Restrained As A Distinctive Group-Founded Attitude To Intensification, In Which The Exploration Procedure Is Stimulated By The Progression Of Factual Sweetie-Bee Marital And Imitator The Iterative Coupling Progression Of Honey Bees And Schemes To Excellent Qualified Drones For Copulating Progression Through The Aptness Functions Enrichment For Mixture Of Greatest Weights For Secreted Layers Of NN Classifiers. Advanced HBMO (AHBMO-NN) Procedure Is Nowadays Realistic To Categorize The Information Efficiently Through Teaching The Neural System. The Arrangement Precision Of AHBMO-NN Is Associated With Several Other Procedures. In This Work, Promoted Honey-Bee Coupling Optimization Process (AHBMO-NN) Is Offered And Verified By Few Benchmark Instances. A Developed Way Of Honey Bee Mating Optimization Performance Is Joined With Neural Network Which Increases Exactitude And Decrease Time Interruption In Complication Of Numerous Factual World Datasets.

Deriving optimal operation of reservoir proposing improved artificial bee colony algorithm: standard and constrained versions

Abstract In this paper, one of the newest meta-heuristic algorithms, named artificial bee colony (ABC) algorithm, is used to solve the single-reservoir operation optimization problem. The simple and hydropower reservoir operation optimization problems of Dez reservoir, in southern Iran, have been solved here over 60, 240, and 480 monthly operation time periods considering two different decision variables. In addition, to improve the performance of this algorithm, two improved artificial bee colony algorithms have been proposed and these problems have been solved using them. Furthermore, in order to improve the performance of proposed algorithms to solve large-scale problems, two constrained versions of these algorithms have been proposed, in which in these algorithms the problem constraints have been explicitly satisfied. Comparison of the results shows that using the proposed algorithm leads to better results with low computational costs in comparison with other available methods such as genetic algorithm (GA), standard and improved particle swarm optimization (IPSO) algorithm, honey-bees mating optimization (HBMO) algorithm, ant colony optimization algorithm (ACOA), and gravitational search algorithm (GSA). Therefore, the proposed algorithms are capable algorithms to solve large reservoir operation optimization problems.

A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm

SummaryOne of the most critical issues in using service‐oriented technologies is the combination of services, which has become an important challenge in the present. There are some significant challenges in the service composition, most notable is the quality of service (QoS), which is more challenging due to changing circumstances in dynamic service environments. Also, trust value in the case of selection of more reliable services is another challenge in the service composition. Due to NP‐hard complexity of service composition, many metaheuristic algorithms have been used so far. Therefore, in this paper, the honeybee mating optimization algorithm as one of the powerful metaheuristic algorithms is used for achieving the desired goals. To improve the QoS, inspirations from the mating stages of the honeybee, the interactions between honeybees and queen bee mating and the selection of the new queen from the relevant optimization algorithm have been used. To address the trust challenge, a trust‐based clustering algorithm has also been used. The simulation results using C# language have shown that the proposed method in small scale problem acts better than particle swarm optimization algorithm, genetic algorithm, and discrete gbest‐guided artificial bee colony algorithm. With the clustering and reduction of the search space, the response time is improved; also, more trusted services are selected. The results of the simulation on a large‐scale problem have indicated that the proposed method is exhibited worse performance than the average results of previous works in computation time.

Discrete honey bees mating optimization (DHBMO) for solving environmental optimization problem using single and multi-objectives optimization

One of the recently proposed algorithms in the field of nature-inspired algorithm is the Honey Bees Mating Optimization (HBMO) algorithm by Haddad and Afshar. The HBMO algorithm simulates the marriage behaviour of bees. The solutions are incrementally constructed by the HBMO algorithm. Therefore, the HBMO algorithm is a swarm-based constructive optimization algorithm. However, no study has reported the modification of HBMO for feature selection. Therefore, we try to develop a discrete honey bees mating optimization (DHBMO) as the modification of HBMO for solving discrete optimization problem especially feature selection to improve the performance of machine vision for predicting water stress in plants. We test the algorithm to solve feature selection problem using single and multi-objectives optimization to optimize water usage in plants for environmental sustainability. The results showed DHBMO has better performance compared to the existed nature-inspired optimization algorithms such as genetic algorithms (GA), simulated annealing (SA) and discrete-particle swarm optimization (discrete-PSO).

An Integrated Solution to Minimize the Energy Consumption of a Resource-Constrained Machining System

Going “energy efficient” has been one of the missions for manufacturers to stay globally competitive. Considering machining as a major manufacturing activity, how to effectively model and control its energy consumption becomes critical. Although researchers have analyzed the energy consumption of machining from the machine, process planning, or shop-floor perspective individually, very litter work has comprehensively studied the concurrent interactions among energy-aware decisions for machine parameter settings, process planning, and shop-floor control. Hence, the work presented in this article undertakes this challenge in the context of a resource-constrained machining system. In particular, the energy characteristics of machining are first analyzed with the consideration of various machine tools, cutting tools, cutting parameters, operation sequences, as well as machine availability. A multiobjective optimization model is then developed to minimize both energy consumption and makespan. The solution is provided through honey bee mating optimization algorithm (HBMOA) combined with shop-floor simulation. In addition, the significance of the proposed approach is exemplified and elucidated by a case study. Note to Practitioners —A well-informed decision made in cutting parameter optimization or process planning relies heavily on the accurate data delivered from the shop floor. In other words, the decision made without the consideration of the shop-floor situation might not achieve the original goal or even fail to materialize. As these factors exist in a real manufacturing cycle, how to integrate shop-floor scheduling with process planning and machining parameter optimization becomes essential for energy-efficient manufacturing. This article undertakes this challenge and develops a multiobjective optimization model, where energy consumption of machining, for the first time to the best of our knowledge, is comprehensively analyzed at both the machine level and shop-floor level. Such broader integration makes this approach more practical and applicable to real industry settings, particularly when the shop-floor resource is limited.

Improving Reliability and Reducing Power Loss in Power Distribution Network by Determining Optimal Location and Size of Capacitor Banks

The use of capacitor banks in distribution system has many outstanding usages include improving the power factor of a system, voltage profile, and reliability besides the reducing of the power flow losses of the component’s reactive due to the compensation. These benefits depend greatly on how capacitors are placed in the distribution system. Hence, in order to achieve the high reliable construction, switching capacitor has been placed to improve the main challenges of the network designing (reliability and reduce power loss) in the radial distribution system. As regards, the importance of the reliability and power losses are ignored in the distribution networks; the aim of this paper is primarily to establish an objective function for the parallel optimization of these aforementioned parameters. In the simulation process, ten parameters have been compared, which are: System Average Interruption Frequent Index (SAIFI) and its cost, System Average Interruption Duration Index (SAIDI) and its cost, power loss and its cost, the installed capacity and it's cost and values of two objective functions. Honey-bee mating optimization (HBMO) algorithm has been used to solve this problem. Then, the developed technique has been used on the IEEE standard distribution network as a problem-solving system.

Searching for common patterns on protein sequences by means of a parallel hybrid honey-bee mating optimization algorithm

Abstract At present, most computing systems have multiple cores accessing to a shared memory space. In the case of computing systems devoted to research, it is common to cluster several shared memory architectures together in order to go beyond the limits of a single shared memory architecture. These clusters can represent a significant resource for those researchers who accomplish to exploit them to the fullest. One of the most applied techniques to achieve this purpose is the mixed mode parallel programming which combines both OpenMP and MPI paradigms. In this paper, we present a parallel implementation of a swarm-based evolutionary algorithm designed for solving a complex biological optimization problem. In order to obtain the maximum possible performance, we have combined MPI and OpenMP. Furthermore, in order to solve the addressed problem in a realistic way, we have applied multiobjective optimization. For assessing the performance achieved by our proposal we have conducted experiments under different systems on six biological instances with different sizes. Results point out the relevance of combining mixed mode parallel programming, a swarm-based evolutionary algorithm, and multiobjective optimization from both parallelism and biology points of view.

Identification of Disease Critical Genes Using Collective Meta-heuristic Approaches: An Application to Preeclampsia.

Identifying a small subset of disease critical genes out of a large size of microarray gene expression data is a challenge in computational life sciences. This paper has applied four meta-heuristic algorithms, namely, honey bee mating optimization (HBMO), harmony search (HS), differential evolution (DE) and genetic algorithm (basic version GA) to find disease critical genes of preeclampsia which affects women during gestation. Two hybrid algorithms, namely, HBMO-kNN and HS-kNN have been newly proposed here where kNN (k nearest neighbor classifier) is used for sample classification. Performances of these new approaches have been compared with other two hybrid algorithms, namely, DE-kNN and SGA-kNN. Three datasets of different sizes have been used. In a dataset, the set of genes found common in the output of each algorithm is considered here as disease critical genes. In different datasets, the percentage of classification or classification accuracy of meta-heuristic algorithms varied between 92.46 and 100%. HBMO-kNN has the best performance (99.64-100%) in almost all data sets. DE-kNN secures the second position (99.42-100%). Disease critical genes obtained here match with clinically revealed preeclampsia genes to a large extent.

Loss minimization in distribution systems based on LMP calculation using honey bee mating optimization and point estimate method

Locational marginal price (LMP) calculation at distributed generation (DG) busses is an effective way for optimal operation of DG units. The aim of this study is presenting a new LMP formulation which includes distribution loss minimization. A Meta-heuristic modified honey bee mating optimization (MHBMO) algorithm is used to obtain optimal values of LMP and power factor of DG units so that the loss of network is minimized, and the benefit of DG units is maximized. Furthermore, a stochastic power flow based on the point estimate method (PEM) is located in MHBMO algorithm to capture uncertainty in the market price and load demand. The proposed policy is applied to a realistic distribution system for the various market prices at substation buses. The results of this study prove the accuracy and efficiency of the new method over the previous methods in terms of the DG units' benefit, loss minimization, and state estimation of the system.