Bee Colony Optimization Research Articles

Integrating machine learning and bee algorithms with multi-agent systems for dynamic vehicle routing problem with time windows

Integrating machine learning and bee algorithms with multi-agent systems for dynamic vehicle routing problem with time windows

Synthetic Data Generation with Modified Artificial Bee Colony Optimization Algorithm and Statistical Modeling

Machine learning is a powerful decision support system used in analyzing and evaluating real-life data. This system aims to create new solutions and improve performance. Therefore, it is related to the field of data science. There are data on the basis of this relationship The effectiveness of drawing meaningful insights from data depends on the quality of the model's training. To improve this performance, the variety of combinations among the data and the total number of data in the dataset should be increased. But in this topic, insufficient data access, legal regulations, ethical rules, confidentiality procedures, privacy, data sharing restrictions and cost parameters are obstacles. Synthetic data generation is a basic step in the field of data science in order to solve all these problems, improve functionality and provide powerful machine-learning inferences. Therefore, a new synthetic data generation approach consisting of 3 basic stages is proposed in this study. In the first stage, synthetic data production similar to the distribution of the original data was carried out with the modified ABC (Artificial Bee Colony) optimization algorithm. In the second stage, the category information of the independent variables was determined by the statistical evaluation analyzed with regression methods among the artificial data produced. In the third stage, the efficiency and applicability of the artificial data produced were evaluated with supervised machine learning classifiers. As a result of the evaluation, it has been proven that the proposed synthetic data generation approach improves the performance of machine learning classifiers in proportion to the increasing number of data. The decision tree algorithm that showed maximum performance produced success rates of 100%, 92.5%, 100%, 85%, and 66% on 5 separate enriched datasets, respectively.

Lifetime maximization of IoT-enabled smart grid applications using error control strategies

Recently, with the advancement of Internet of Things (IoT) technology, IoT-enabled Smart Grid (SG) applications have gained tremendous popularity. Ensuring reliable communication in IoT-based SG applications is challenging due to the harsh channel environment often encountered in the power grid. Error Control (EC) techniques have emerged as a promising solution to enhance reliability. Nevertheless, ensuring network reliability requires a substantial amount of energy consumption. In this paper, we formulate a Mixed Integer Programming (MIP) model which considers the energy dissipation of EC techniques to maximize IoT network lifetime while ensuring the desired level of IoT network reliability. We develop meta-heuristic approaches such as Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) to address the high computation complexity of large-scale IoT networks. Performance evaluations indicate that the EC-Node strategy, where each IoT node employs the most energy-efficient EC technique, yields a minimum of 8.9% extended lifetimes compared to the EC-Net strategies, where all IoT nodes employ the same EC method for a communication. Moreover, the PSO algorithm reduces the computational time by 77% while exhibiting a 2.69% network lifetime decrease compared to the optimal solution.

Radar Based Secure Contactless Fall Detection Using Hybrid Optimizer with Convolution Neural Network

Introduction: senior citizens can lead to severe injuries. Existing wearable fall-alert sensors are often ineffective as seniors tend to avoid using them, highlighting the need for non-contact sensor applications in smart homes. This study proposes a CNN-based fall detection system using time-frequency analyses. A unique hybrid optimizer, GWO-ABC, combining Artificial Bee Colony (ABC) and Grey Wolf Optimizer (GWO), is employed to optimize CNN architectures. Radar return signals are transformed into spectrograms and binary images for training the HOCNN with fall and non-fall data.Methods: Radar signals are processed using short-time Fourier transformation to create time-frequency spectrograms, converted into binary images. These images are fed into a CNN optimized by the GWO-ABC algorithm. The CNN is trained on labelled fall and non-fall instances, focusing on high-level feature extraction.Results: The HOCNN showed superior accuracy in fall detection, successfully extracting critical high-level features from radar signals. Performance metrics, including precision, recall, and F1-score, demonstrated significant improvements over traditional methods.Conclusion: This study introduces a non-contact, automatic fall detection system for smart homes using GWO-ABC optimized CNNs, offering a promising solution for enhancing geriatric care and ensuring senior citizen safety. Index Terms—Grey Wolf Optimizer, Artificial Bee Colony algorithm, Convolutional neural network, fall detection, time-frequency analysis, ultra-wideband (UWB) radar

Solving a Stochastic Multi-Objective Sequence Dependence Disassembly Sequence Planning Problem with an Innovative Bees Algorithm

As the number of end-of-life products multiplies, the issue of their efficient disassembly has become a critical problem that urgently needs addressing. The field of disassembly sequence planning has consequently attracted considerable attention. In the actual disassembly process, the complex structures of end-of-life products can lead to significant delays due to the interference between different tasks. Overlooking this can result in inefficiencies and a waste of resources. Therefore, it is particularly important to study the sequence-dependent disassembly sequence planning problem. Additionally, disassembly activities are inherently fraught with uncertainties, and neglecting these can further impact the effectiveness of disassembly. This study is the first to analyze the sequence-dependent disassembly sequence planning problem in an uncertain environment. It utilizes a stochastic programming approach to address these uncertainties. Furthermore, a mixed-integer optimization model is constructed to minimize the disassembly time and energy consumption simultaneously. Recognizing the complexity of the problem, this study introduces an innovative bees algorithm, which has proven its effectiveness by showing a superior performance compared to other state-of-the-art algorithms in various test cases. This research offers innovative solutions for the efficient disassembly of end-of-life products and holds significant implications for advancing sustainable development and the recycling of resources.

Comparing the performance of metaheuristics on the Transit Network Frequency Setting Problem

The Transit Network Frequency Setting Problem (TNFSP), an NP-Hard combinatorial optimization problem, has been frequently addressed in previous investigations, most of which employ metaheuristics. However, previous studies have not investigated to determine the best metaheuristic and the most effective parameter values for the TNFSP. This study aims to fill this research gap by comprehensively comparing five well-known metaheuristics, namely, Artificial Bee Colony, Differential Evolution, Firefly Algorithm, Genetic Algorithm, and Particle Swarm Optimization, on the TNFSP. The best parameter configurations for employed metaheuristics are determined using the brute-force method. The comparative results not only highlight the significance of metaheuristic selection in addressing the TNFSP but also reveal the importance of choosing the most suitable parameter values for these metaheuristics. Furthermore, the metaheuristics are performed on the transit networks obtained from previously published studies for comparison purposes. The results demonstrate that the metaheuristics achieve better frequency sets than the original frequency sets of the used transit networks.

Human-Robot Collaboration in Mixed-Flow Assembly Line Balancing under Uncertainty: An Efficient Discrete Bees Algorithm

In the evolving landscape of manufacturing and remanufacturing, assembly lines play a crucial role. Within the context of Industry 5.0, human workers are seen as a valuable and irreplaceable resource. Human-robot collaboration is a promising production model that combines the strengths of human workers and robots, thereby enhancing production efficiency while reducing occupational risks related to ergonomics. Despite these advancements, inherent uncertainties within assembly processes, the integration of human-robot partnerships, and the dynamic nature of market demands pose significant challenges to traditional assembly methods. To address these challenges, this research introduces a novel modelling approach through a mixed-flow assembly line balancing problem designed for uncertain environments, fostering collaboration between humans and robots. The primary goal is to facilitate efficient collaboration within a type-I assembly line balancing problem framework, where predefined assembly beats guide the workflow. In this research, the use of interval type-2 fuzzy sets capabilities was investigated to address uncertainties in the assembly process. Furthermore, the potential of pairing human operators of different abilities with robots of different models for collaborative tasks at workstations was explored, enhancing flexibility and adaptability in the assembly line. In response to the complexity of the problem, this research proposes an efficient multiobjective discrete bees algorithm that incorporates innovative operators and search strategies. Rigorously tested across diverse case studies, this algorithm consistently outperforms other comparator algorithms. This research not only offers novel perspectives on addressing assembly line balancing challenges but also provides valuable insights for the effective implementation of human-robot collaborative assembly in uncertain environments.

Optimising distributed heterogeneous flowshop group scheduling arising from PCB mounting: integrating construction and improvement heuristics

ABSTRACT In real-world manufacturing systems for processing printed circuit boards (PCBs), the workshops integrating various flowline-based cells are common in large-scale enterprises. The scheduling problem within the systems is modelled as the distributed heterogeneous flowshop group scheduling problem (DHFGSP) in this study. Departing from practical requirements and considering the grouping characteristics among PCB components, we account for carryover sequence-dependent setup time (CSDST). Moreover, recognising the critical importance of just-in-time production in semiconductor manufacturing, the total tardiness, a previously unexplored objective within the DHFGSP context, is addressed. To tackle this problem, a mixed-integer linear programming (MILP) model, capable of obtaining optimal solutions for small-scale instances, is proposed. Due to the NP-hard nature of the problem, obtaining high-quality solutions in a reasonable time using the MILP model becomes challenging for large-scale instances. Therefore, a solution algorithm, comprising construction and improvement heuristics, is developed. Capitalising on the problem’s characteristics, the construction heuristic efficiently generates high-quality feasible solutions in a very short time. Built primarily around artificial bee colony (ABC) optimisation, the improvement heuristic can significantly further enhance solutions by integrating the collaborative and restart operators. Comprehensive experiments on instances of varying scales demonstrate the effectiveness of the proposed algorithm for the problem under investigation.

Artificial bee colony optimization algorithm with full dimensional updating strategy and its application

For the low accuracy and slow convergence speed of artificial bee colony(ABC) algorithm in solving complex optimization problems, an improved artificial bee colony(ABC) algorithm based on the new full dimensional updating ABC/best /1 strategy, namely FNABC was proposed in this paper. In the FNABC, for the low efficiency of one-dimensional search, the full dimensional updating search strategy and ABC/best /1 strategy were combined to design a new full dimensional updating ABC/best/1 strategy, which expanded the search space, improved the mining ability and search efficiency. Then, a new evolutionary phase is designed to balance the global search ability and local mining ability to avoid falling into local optimum and improve the convergence accuracy. Finally, the FNABC is compared with eight state-of-the-art ABC variants such as AABC, iqABC, MEABC, ABCVSS, GBABC, DFSABC, MABC-NS, MGABC in solving 12 complex functions. All functions have obtained the best optimal values among 9 algorithms. Additionally, FNABC is applied to solve a real-world train operation adjustment problem. The experiment results indicate that the FNABC has better optimization ability, scalability and robustness. It obtains the ideal train operation adjustment results.

Advanced RUL Estimation for Lithium-Ion Batteries: Integrating Attention-Based LSTM with Mutual Learning-enhanced Artificial Bee Colony Optimization

Advanced RUL Estimation for Lithium-Ion Batteries: Integrating Attention-Based LSTM with Mutual Learning-enhanced Artificial Bee Colony Optimization

Enhanced LA-HBO technique for optimal position of PMU with complete prominence

This manuscript proposes a hybrid technique for determining the optimal positioning of phasor measurement units (PMUs) in power systems. The PMUs play a crucial role in power system control, wide-area monitoring, and protection. The proposed hybrid method is the joint execution of the Lichtenberg algorithm (LA) and the heap-based optimization (HBO) technique. Hence, it is named the LA-HBO technique. The objective of the proposed method is to place the PMUs in the power system for observability. The goal is to enhance the efficiency and accuracy of PMU placement, ensuring optimal positioning for improved grid monitoring capabilities. The Lichtenberg Algorithm (LA) enhances PMU placement by addressing system observability challenges and ensuring that selected locations provide comprehensive coverage of the power grid. The heap-based approach optimizes PMU placement by efficiently managing the selection process, considering factors like computational efficiency and scalability. The proposed hybrid technique is implemented in IEEE-30 and -14 bus systems. The MATLAB-based simulation results are compared with the various existing methods, such as Sea Lion Optimization (SLO), Particle Swarm Optimization (PSO), and Ant Bee Colony Optimization (ABC). By then, the outcome reveals the efficacy of the proposed method for defining the optimum PMU locations. The proposed method shows a low computational time of 0.02348 sec for the IEEE-14 bus, and 0.03565 sec for the IEEE-30 bus compared with other existing methods.

Enhanced Performance of Membrane Bioreactor in Sewage Treatment through Integrated Control Strategy

<p>Wastewater includes sewage water, which presents serious environmental problems that necessitate effective treatment techniques. Membrane Bioreactors (MBRs) have emerged as promising solutions, albeit plagued by membrane fouling and computational loading issues. To resolve these issues, this research article presents an innovative control strategy combining both artificial bee colony optimization (ABC) and recurrent neural network (RNN) to regulate the performance of MBR in sewage treatment. Initially, the influent wastewater data was collected and pre-processed using the regression imputation approach. RNN architecture was designed and trained using the pre-processed data to forecast the performance of the MBN system. Further, the ABC algorithm was applied to optimize the function of MBR by adjusting the control variables. The developed model was validated with the publically available wastewater treatment plan dataset and the effectiveness of the developed model was validated by performing intensive performance and comparative assessment. The performance evaluation demonstrates that the proposed methodology attained greater results of 98.59% effluent quality, 98.70% of nutrient removal efficiency, less computational time of 2.87s, and a low membrane fouling index of 1.23%. The comparative analysis illustrates that the presented approach achieved improved performances than the existing methodologies.</p>

Surrogate modeling for aerodynamic static instability of central-slotted box decks using machine learning approaches

Studies on aerodynamic controls of central-slotted box decks primarily focused on mitigating vortex-induced vibrations (VIV), as this type of deck typically performs well against flutter instability. However, as the span length increases, the critical wind speed of aerodynamic static instability ( U cr) might be lower than flutter critical wind speed. Thus, U cr will determine the overall aerodynamic performance of such bridges. Investigating this instability through wind tunnel testing methods and numerical simulation can be expensive and time-consuming. In this paper, surrogate models using machine learning approaches, specifically artificial neural network (ANN) and extreme gradient boosting (XGBoost), were developed and optimized for fast and reliable prediction for U cr based on wind tunnel tests and simulation data. The results demonstrated that the built surrogate models can predict U cr accurately. The parametric study results showed that the height ratio of wind fairing apex ( a/b), wind angle of attack ( α), and length of the main span ( L) have the most influence on the U cr compared with other parameters. Finally, based on the developed ANN surrogate model and the artificial bee colony (ABC) optimization algorithm, an optimized section was proposed to enhance the section’s performance against aerodynamic static instability.

Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River

The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the Çoruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t − 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = − 251.090, Bias Factor = − 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources.

Design optimization of a wearable chair using The Bees Algorithm

A wearable chair is designed to reduce musculoskeletal disorders in workers. Two-staged optimization process using The Bees Algorithm is conducted to determine the lowest operable height of the chair and minimize the maximum force among the three supports. Geometric, dimensional, and rotational angle constraints are defined accordingly. A CAD model of the chair is imported into ADAMS software for simulation. The simulation results identified the most stressed rod in the chair. A spring is selected and implemented in ADAMS to counteract the applied force on this rod. A comparison between the force on the most stressed support and the spring force through a defined motion revealed minimal differences, indicating that the spring effectively counteracted the applied force. The results demonstrated the successful application of The Bees Algorithm to wearable chairs and the successful implementation of a spring to counteract applied force, thereby achieving the objectives of this study.

Dynamic payment on microtasking platforms using bee colony optimization

Microtasking involves breaking down a job into smaller tasks and assigning them to a group of workers who voluntarily complete the tasks and receive payment. However, the joint estimation of quality and determination of payment remains an underexplored area in microtask crowdsourcing research. This paper addresses the limitation of unestimated payment on microtasking platforms by introducing a dynamic worker quality and payment estimation algorithm known as Dynamic Quality Payment (DQP). Utilizing the Bee Colony Optimization (BCO) algorithm, the proposed approach integrates worker quality estimation and payment determination. DQP incorporates the Gaussian Process Model (GPM) to initially estimate worker quality and then optimize payments based on the quality of work. Empirical testing of the algorithm is conducted using two datasets from Amazon Mechanical Turk. Additionally, the DQP algorithm is compared against novel microtasking and quality estimation algorithms, demonstrating superior performance. The experimental results illustrate that DQP not only reduces the cost of microtasking but also accurately estimates worker quality.

Bee Algorithm Based Control Design for Two-links Robot Arm Systems

Bee Algorithm Based Control Design for Two-links Robot Arm Systems

Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction.

Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD) techniques, to model groundwater levels in Erzurum province, Türkiye. GWL estimation results were evaluated with mean square error (MSE), coefficient of determination (R2), and residual sum of squares (RSS) and visually with violin, scatter, and time series plot. The study results indicated that the EEMD-ABC-ANN hybrid model was superior to other models in estimating GWL, with R2 values ranging from 0.91 to 0.99 and MSE values ranging from 0.004 to 0.07. It has also been revealed that promising GWL predictions can be made with previous GWL data.

Optimizing Forest Surveillance: A Hybrid Algorithm Combining ACO and ABC

This work introduces a potential Hybrid Algorithm for the complex field of forest monitoring, integrating Artificial Bee Colony Optimisation (ABC) and Ant Colony Optimisation (ACO). The performance indicators of the algorithm were carefully assessed in a fictitious use case. Its effectiveness was demonstrated by a shorter drone path, a shorter flight duration, and less energy usage, making it an affordable surveillance option. In addition, the algorithm demonstrated a great rate of mission coverage, quick convergence, and consistently good quality of solutions. Its usefulness in dynamic forest habitats was highlighted by its capacity to adjust to changing weather conditions and scale to accommodate more waypoints. Its cost-effectiveness is increased by efficient resource utilisation, which is demonstrated by low CPU and memory consumption. Taken together, these results highlight how the algorithm may transform forest surveillance by increasing operational effectiveness, cutting expenses, and satisfying the changing requirements of intricate monitoring scenarios. To fully realise the algorithm's potential for environmental monitoring applications, this research advocates for more real-world testing and optimisation.

Augmenting Cervical Cancer Analysis with Deep Learning Classification and Topography Selection Using Artificial Bee Colony Optimization

Augmenting Cervical Cancer Analysis with Deep Learning Classification and Topography Selection Using Artificial Bee Colony Optimization