Intelligent Engineering Optimisation with the Bees Algorithm
- 2025
- Book
- Editors
- D. T. Pham
- Natalia Hartono
- Book Series
- Springer Series in Advanced Manufacturing
- Publisher
- Springer Nature Switzerland
About this book
This book presents new and advanced results and developments related to the Bees Algorithm, along with its application to a wide range of engineering problems.
Modern complex processes and systems are difficult to optimise using conventional mathematical tools as they require models that often cannot be obtained with accuracy or certainty. Optimising such systems demands efficient, model-free optimisation tools.
The Bees Algorithm, a swarm-based technique inspired by the foraging behaviour of honeybees, is an ideal tool for tackling challenging optimisation problems. The algorithm is conceptually elegant and extremely easy to apply. All it needs to solve an optimisation problem is a means to evaluate the quality of potential solutions.
While the covered applications belong to diverse engineering fields, this book’s focus is on advanced manufacturing and industrial engineering. The book comprises two parts. The first part explores different enhancements made to the original Bees Algorithm to improve its performance.
The second part delves into the algorithm's applications in design, manufacturing, production, ergonomics, logistics, transportation, and electrical and electronic engineering.
By showcasing the variety of optimisation tasks successfully handled using the Bees Algorithm, the book aims to inspire and motivate engineers and researchers worldwide to adopt the algorithm as a powerful and versatile tool for conquering complex engineering problems in the Industry 4.0 era and beyond.
Advertisement
Table of Contents
-
Frontmatter
-
Bees Algorithm Development
-
Frontmatter
-
Enhanced Bees Algorithm Implementing Early Neighbourhood Search with Efficiency-Based Recruitment
Michael S. Packianather, Azar Imanguliyev, D. T. PhamThe chapter focuses on enhancing the Bees Algorithm, a stochastic optimisation technique inspired by honey bee foraging behaviour. It introduces early neighbourhood scanning to start the search from more promising locations and efficiency-based recruitment to dynamically allocate bees based on site efficiency. These improvements aim to enhance the algorithm's performance, particularly in high-dimensional problems. The chapter also provides a detailed overview of the Bees Algorithm, its working mechanisms, and the computational modelling of honey bee foraging behaviour. By implementing these strategies, the enhanced Bees Algorithm seeks to improve the efficiency and effectiveness of the optimisation process.AI Generated
This summary of the content was generated with the help of AI.
AbstractThis chapter discusses modifications to the basic Bees Algorithm to enhance its performance. The proposed changes involve adding an early neighbourhood scanning step to determine promising areas from which to start the local search operation and making the recruitment process dynamic by varying the number of bees recruited to exploit a patch according to how efficiently it has been exploited. The enhanced version of the Bees Algorithm was applied to well-known complex continuous benchmark functions with high dimensions to find their optima. The results obtained from this study show that the new Bees Algorithm performed better than the basic version. The results were also compared with those of other swarm-based optimisation methods. -
Improving the Bees Algorithm Using Gradual Search Space Reduction
Turki Albakr, D. T. PhamThe chapter delves into the challenges faced by the Bees Algorithm, a swarm intelligence-based optimization technique, including slow convergence and local optima traps. It introduces a gradual search space reduction method inspired by region elimination techniques, adapting it for multi-optima problems. The method systematically reduces the search space to enhance the algorithm's efficiency and convergence rate. The proposed approach is evaluated using 24 benchmark functions, demonstrating its effectiveness compared to the basic Bees Algorithm and other well-known optimization algorithms like PSO and ABC. The results highlight improvements in solution accuracy, success rate, and the number of function evaluations, making the chapter a valuable resource for optimization specialists and researchers.AI Generated
This summary of the content was generated with the help of AI.
AbstractThe Bees Algorithm is a well-known metaheuristic optimisation method that has been applied in many disciplines with noticeable success. For example, it has been applied to machine scheduling, training artificial neural networks (ANNs) for pattern recognition, and the design of mechanical structures. There have been many attempts to improve the Bees Algorithm’s performance to tackle some of its weaknesses with more focus on the local search stage. This research attempts to improve the Bees Algorithm with more attention directed to stages other than the local search. The suggested method employs an adapted notion of the regional elimination method to achieve the abandonment and reduction of the search space within the Bees Algorithm. To assess the performance, the proposed method was tested on 24 benchmark functions, and it was applied to two engineering problems. The results obtained indicate a statistically significant improvement. -
Local Optimal Issue in Bees Algorithm: Markov Chain Analysis and Integration with Dynamic Particle Swarm Optimisation Algorithm
JianBang Liu, Mei Choo Ang, Kok Weng Ng, Jun Kit ChawThe chapter delves into the behaviour of bee colonies as a model for optimisation problems, focusing on the Bees Algorithm (BA) inspired by bee foraging. It discusses the limitations of BA, particularly the local optimal issue, and introduces Markov Chain analysis to understand the algorithm's convergence properties. The integration of Dynamic Particle Swarm Optimisation with BA is presented as a solution to enhance the algorithm's ability to escape local optima while maintaining convergence performance. The chapter also explores strategies such as neighbourhood contraction and site abandonment to improve BA's performance, making it a valuable resource for researchers and practitioners in the field of optimisation algorithms.AI Generated
This summary of the content was generated with the help of AI.
AbstractInspired by the foraging behaviour of bees in nature, the bees algorithm (BA) is a biomimetic optimisation method designed according to how bees work together in choosing food sources and collecting high-quality honey. However, there are only a few mathematical studies on the convergence and optimisation problem of BA in the literature, and most of the BA implementations are dependent on trial-and-error. Thus, this study attempts to use Markov chain theory to enhance the understanding and analysis of BA convergence from the perspectives of the neighbourhood contraction strategy and site abandonment strategy. To improve the optimisation performance of the BA, this study established a combination model of the BA and a dynamic particle swarm optimisation algorithm on the local search process of the BA and the effect of the task allocation of scout bees for target location. Finally, the simulation experiment was conducted in MATLAB using the combination model. The simulation result shows that the probability of the combination model falling into the local optimum is much lower than that of the conventional BA based on the good convergence performance for the optimisation problem. It can enrich the theoretical base of the BA as a resource for convergence performance and further optimisation analysis. -
Development of the Bees Algorithm Toolkit for Optimisation in LabVIEW
Murat Sahin, D. T. PhamThe chapter introduces the Bees Algorithm (BA), inspired by honey bees' foraging behavior, and its implementation in LabVIEW for optimization purposes. It covers the local search mechanism of BA, the development of a LabVIEW toolkit for continuous and combinatorial problems, and experimental results demonstrating the toolkit's effectiveness. The toolkit enables users to define new functions and perform optimizations efficiently, showcasing its competitiveness with other metaheuristic algorithms.AI Generated
This summary of the content was generated with the help of AI.
AbstractThis chapter presents a Bees Algorithm (BA) Optimisation Toolkit developed in LabVIEW. The BA is an effective optimisation algorithm that mimics the nectar-foraging behaviour of honey bees. LabVIEW is a powerful program for data acquisition and control applications that is very popular in industry. There are tools within the scope of optimisation within LabVIEW, but there is no toolkit for the BA. In this chapter, the preparation of the BA in LabVIEW is explained step by step. The toolkit has two parts. Optimisation can be performed on standard continuous test functions with the first part, and new functions and problems can be defined and solved. It has been observed that the minimum values of complex continuous functions can be reached quickly in experimental studies. With the second part, combinatorial optimisation can be conducted on travelling salesman problems (TSPs), and new TSPs can be defined and solved. It has been shown experimentally that minimum-cost solutions can be found in a small number of iterations using this toolkit.
-
-
Engineering Applications of the Bees Algorithm
-
Frontmatter
-
Geometrical Optimisation of Smart Sandwich Plates Using the Bees Algorithm
Nguyen Dinh Duc, Tran Quoc QuanThis chapter delves into the geometrical optimization of smart sandwich plates using the Bees Algorithm, focusing on two primary types: magneto–electro–elastic plates with auxetic honeycomb cores and piezoelectric plates with porous homogeneous cores and carbon nanotube-reinforced nanocomposite layers. The study introduces a new model for these smart materials, deriving analytical expressions for natural frequency and critical buckling load. The Bees Algorithm is employed to determine optimal geometrical and material parameters, resulting in significant improvements in structural performance. The chapter presents detailed numerical results and convergence analyses, highlighting the effectiveness of the Bees Algorithm in maximizing critical buckling load and natural frequency. The findings provide valuable guidelines for engineers and designers in the development and application of smart materials in various industries.AI Generated
This summary of the content was generated with the help of AI.
AbstractThe geometrical optimisation of smart sandwich plates on Pasternak-type elastic foundations is presented in this chapter. The basic equations are derived based on Reddy’s higher-order shear deformation plate theory and Hamilton’s principle, taking into account the effect of von Kármán kinematic nonlinearity. The forms of possible solutions and electric and magnetic potentials are chosen as double trigonometric functions based on boundary conditions. The expressions of the natural frequency and the critical buckling load are determined by using the Galerkin method. The Bees Algorithm is applied to obtain the maximum values of the natural frequency and the critical buckling load of the sandwich plate, which depends on geometrical and material parameters. -
Integrating the Bees Algorithm with WSAR for Search Direction Determination and Application to Constrained Design Optimisation Problems
Adil Baykasoglu, Mumin Emre SenolThis chapter delves into the integration of the Bees Algorithm (BA) and the Weighted Superposition Attraction-Repulsion (WSAR) algorithm to enhance the search direction determination mechanism of BA. The hybrid approach, termed hBA, leverages the superposition principles of WSAR to guide the search process of BA, resulting in improved performance for constrained design optimization problems. The chapter provides a detailed overview of both BA and WSAR, explaining their mechanisms and applications in various engineering problems. The computational study demonstrates the superior performance of hBA compared to other metaheuristic algorithms, including the basic BA, on four benchmark engineering design problems. The results highlight the potential of hybridization in metaheuristics to achieve better optimization outcomes.AI Generated
This summary of the content was generated with the help of AI.
AbstractThe bees algorithm (BA) is a novel swarm-based intelligent metaheuristic search algorithm that was proposed by Pham et al. in 2005. It has been applied to several complex optimisation problems successfully. One of the main mechanisms in BA is to direct search agents (bees) to the search sites that were discovered by the best-performing bees (elite bees). In the original BA, this mechanism is realised by generating random bees in the close neighbourhood of elite bees. In this chapter, a different approach borrowed from the weighted superposition attraction–repulsion algorithm (WSAR) is incorporated into BA for search direction determination. In this approach, attractive and repulsive superpositions are determined by considering “elite bees” and “nonselected site bees” (the worst performing bees), similar to WSAR. The performance of this new approach is tested on four different constrained engineering design optimisation problems. The obtained results are compared with the basic BA and some other metaheuristic algorithms from the literature. The performance of BA can be further improved by utilising such an approach. -
Bees Algorithm-Based Optimisation of Welding Sequence to Minimise Distortion of Thin-Walled Square Al–Mg-Si Alloy Tubes
Chunbiao Wu, Chao WangThe chapter focuses on the optimisation of welding sequences to minimise distortion in thin-walled square Al–Mg–Si alloy tubes using the Bees Algorithm (BA). It begins by highlighting the importance of welding sequence optimisation (WSO) in reducing residual stresses and distortions caused by welding processes. The study introduces the BA, which is inspired by the foraging behaviour of honeybees, as a more efficient alternative to traditional genetic algorithms. The BA is employed to search for optimal welding sequences that minimise distortion, and the results are validated through experimental measurements and finite element analysis. The chapter also discusses the development of an artificial neural network (ANN) model to predict welding distortions accurately, further enhancing the efficiency of the optimisation process. The comparative study demonstrates that the BA-based optimisation framework significantly improves computational accuracy and efficiency, making it a valuable tool for practical engineering applications.AI Generated
This summary of the content was generated with the help of AI.
AbstractThe welding sequence has a significant influence on the magnitude and distribution of the final welding distortion, which often contributes negatively to the dimensional accuracy, structural instability, fabrication cost, and in-service performance of the welded assembly. Hence, it is of great significance to automatically plan optimal welding robot paths for minimum residual deformations in real-world engineering. In this study, an integrated optimisation approach combining an artificial neural network (ANN) model and intelligent optimisation algorithm was proposed to optimise welding sequences for minimising residual distortions in the assembly of thin-walled aluminium tubes. During the optimisation process, an ANN model was successfully established and implanted into this optimisation method to rapidly predict welding distortion within good computational precision. Subsequently, the Bees Algorithm (BA) was developed to systematically search for the optimal welding sequence. This algorithm takes advantage of performing a multi-neighbourhood search combined with a randomised global search to improve computational efficiency and solve premature convergence problems. Finally, a series of experiments were conducted to confirm the accuracy of the proposed BA. Verification experiments demonstrate that the acquired optimum welding sequence was in good agreement with the experimental results. -
Hybrid Genetic Bees Algorithm (GBA) for Continuous and Combinatorial Optimisation Problems
Tran Duc Vi, Vu Hoai Anh Thu, Truong Tran Mai Anh, Nguyen Dao My VyThe chapter introduces the Hybrid Genetic Bees Algorithm (GBA), which combines the Bees Algorithm with genetic algorithms to improve optimization performance. The GBA is applied to continuous optimization problems like training multilayer perceptrons and to combinatorial problems such as the Two-Stage Flow Shop Scheduling Problem (TFSSP) and the Just-In-Time Job Shop Scheduling Problem (JITJSSP). The algorithm's effectiveness is demonstrated through experimental results, showing improved accuracy and lower error rates compared to other methods. The chapter also discusses the advantages of the GBA, including its ability to optimize high variance in random global searches and its strong exploration and exploitation capabilities.AI Generated
This summary of the content was generated with the help of AI.
AbstractThe Bees Algorithm (BA) has proven its effectiveness in performing optimisation in many problems in recent years with many applications, but the global search of the BA is not its strength. This chapter summarised reports on a hybrid model called the Genetic Bees Algorithm (GBA) to enhance the BA for both continuous and combinatorial optimisation problems. For continuous problems, GBA in training a multilayer perceptron (MLP) is reported for the first time to solve real-world problems. Experimental results show that GBA provides significantly better performance than Particle Swarm Optimisation (PSO) in training MLP with higher accuracy. For combinatorial problems, in the two-stage flow shop scheduling problem (TFSSP), three methods, such as Particle Swam Optimiser (PSO), Genetic Algorithm (GA) and GBA, are compared. GBA is competitive and better than PSO and GA in a majority of instances in terms of results, proving that GBA is a realistic and efficient solution to the TFSSP. Finally, GBA proves its strength in solving the Just-in-Time Job Shop Scheduling Problem (JITJSSP) by solving a set of 36 benchmark instances ranging from 20 to 200 operations, the outcomes obtained from that are then compared to an exact method, two recent studies, and best-known solutions. The results show that the GBA has its strengths and weaknesses in solving the JITJSSP, and it performs well in some instances but does not perform well in others. -
Optimisation of Surface Roughness in 3D Printing Using the Bees Algorithm
Shafie Kamaruddin, Arman Hilmi Ridzuan, Nor Aiman SukindarThe chapter delves into the optimization of surface roughness in 3D printing, specifically focusing on the use of the Bees Algorithm as an innovative optimization technique. It begins by introducing the concept of additive manufacturing and its advantages over traditional subtractive methods. The Bees Algorithm, inspired by the foraging behavior of honeybees, is then detailed, highlighting its potential for optimizing 3D printing parameters. The study outlines the methodology, including parameter initialization and the implementation of the Bees Algorithm using R Software. Experimental setups and results are presented, showcasing the algorithm's effectiveness in finding optimal parameter combinations for minimizing surface roughness. The chapter concludes by comparing the Bees Algorithm's performance with other optimization methods, demonstrating its accuracy and potential for industrial applications. The findings suggest that the Bees Algorithm offers a promising alternative for improving the quality of 3D printed products and reducing manufacturing costs.AI Generated
This summary of the content was generated with the help of AI.
AbstractAdditive manufacturing (AM) is renowned for its capability to produce parts that are low-cost and have less manufacturing time. One of the main challenges in this additive manufacturing technology is selecting proper input process parameters to achieve good quality of the 3D printed model. The focus of this study is to determine the optimum input parameter of the 3D printer using the Bees Algorithm (BA). This study uses the Bees Algorithm to predict the best combination parameters to optimise the surface roughness of parts printed by a fused deposition modelling (FDM) machine. The predicted results are compared with the experimental 3D model sample and previous findings of other optimisation methods. Comparative analysis between predicted and actual surface roughness measurements showed good agreement with differences of less than 2%, indicating a significant prediction method. The result also shows that the Bees Algorithm found a better combination of parameters compared to other algorithms. This research provides another alternative optimisation approach for industries that utilise 3D printing. -
The Bees Algorithm for Robotics-Enabled Collaborative Manufacturing
Wenjun Xu, Hang Yang, Zhenrui Ji, Zhihao Liu, Jiayi LiuThe chapter delves into the application of the Bees Algorithm (BA) in robotics-enabled collaborative manufacturing, highlighting its use in solving complex optimisation problems. It covers robotic collaborative manufacturing (RC-Mfg) and human–robot collaborative manufacturing (HRC-Mfg), addressing issues such as multirobot manufacturing optimisation, decision-making in HRC-Mfg, and robotics-related manufacturing service reconfigurations. The framework proposed includes cross-layer optimisation methods and task sequence planning for HRC-Mfg, with case studies demonstrating the effectiveness of BA in improving manufacturing efficiency and optimising task sequences. The chapter also discusses the future potential of combining BA with deep learning and reinforcement learning to tackle more complex problems in this field.AI Generated
This summary of the content was generated with the help of AI.
AbstractRobotics-enabled collaborative manufacturing is vital to improving the efficiency and flexibility of industrial manufacturing processes and realising the digitalisation and intelligentisation of industry. In recent years, enabled by intelligent optimisation algorithms, robotics-enabled collaborative manufacturing approaches have been steadily developed as promising solutions to support industries. However, each robotics-enabled collaborative manufacturing approach has some optimisation problems, limiting its applicability in practice. To address this issue, in this chapter, the Bees Algorithm and robotics-enabled collaborative manufacturing are integrated as an effective approach. In this approach, the efficiency and flexibility of robotics-enabled collaborative manufacturing are significantly improved. Robotics-enabled collaborative manufacturing is composed of robotic collaborative manufacturing and human–robot collaborative manufacturing. In terms of robotic collaborative manufacturing, the improved multiobjective discrete Bees Algorithm and the improved discrete Bees Pareto algorithm are utilised to solve the robotic disassembly line balancing optimisation problem and multiobjective robotic collaborative manufacturing service aggregation optimal selection problem. In terms of human–robot collaborative manufacturing, the Pareto-based modified discrete Bees Algorithm is used to solve the task sequence planning optimisation problem. Afterwards, two case studies are presented to show the performance of the Bees Algorithm in robotics-enabled collaborative manufacturing. The results demonstrate that the Bees Algorithm is superior to some other comparative approaches. -
Bees Algorithm for Hyperparameter Search with Deep Learning to Estimate the Remaining Useful Life of Ball Bearings
Anurakt Kumar, Satyam Kumar, Neha Gupta, Nathinee Theinnoi, D. T. PhamThe chapter explores the use of the Bees Algorithm (BA 2) for hyperparameter search in deep learning models to estimate the Remaining Useful Life (RUL) of ball bearings. It highlights the importance of predictive maintenance in manufacturing and the challenges associated with RUL prediction. The authors propose a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) to analyze vibration signals from bearings and predict their RUL. The Bees Algorithm is employed to optimize the hyperparameters of these deep learning models, enhancing their performance and accuracy. The study compares the results with the IEEE PHM 2012 Prognostic challenge, demonstrating significant improvements in RUL prediction. This approach offers a promising solution for predictive maintenance, reducing downtime and maintenance costs in industrial settings.AI Generated
This summary of the content was generated with the help of AI.
AbstractHyperparameter searching is one of the significant challenges in training deep learning models. To solve this challenge, the Bees Algorithm (BA), which simulates the foraging behaviour of honey bees, is used for hyperparameter searching and finding the best set of hyperparameters for a given deep learning model. This study applies a two-parameter version of the Bees Algorithm (BA2) to search for the best set of hyperparameters for a Convolutional Neural Network (CNN) combined with a Long Short-Term Memory (LSTM) model. Then, the model is used to predict the remaining useful life (RUL) of ball bearings. BA2 uses the traplining foraging technique of bees to integrate explorative and exploitative search mechanisms, reduce the number of parameters to only two and improve the remaining useful life (RUL) prediction. The algorithm can find a set of hyperparameters that makes the deep learning model perform better than the IEEE PHM 2012 Prognostic challenge winner by 38.97%. -
Bees Local Phase Quantisation Feature Selection for RGB-D Facial Expression Recognition
Seyed Muhammad Hossein Mousavi, Atiye IlanlooThe chapter delves into the challenging task of facial expression recognition, highlighting the importance of accurate feature selection. It introduces the Bees Algorithm as a novel approach for this task, which is compared with other methods such as PCA, Lasso, and PSO. The use of Local Phase Quantisation (LPQ) features and the Kinect sensor for depth data collection is detailed, showcasing the potential of bioinspired algorithms in improving recognition accuracy. The chapter also includes a comprehensive validation section, presenting results and comparisons that underscore the effectiveness of the Bees Algorithm in this domain.AI Generated
This summary of the content was generated with the help of AI.
AbstractFeature selection can be defined as an optimisation problem and solved by bioinspired algorithms. The Bees Algorithm (BA) returns great performance in the feature selection optimisation task. On the other hand, local phase quantisation (LPQ) is a frequency domain feature that has excellent performance on depth images. Here, after extracting LPQ features from RGB (colour) and depth images from the Iranian Kinect Face Database (IKFDB), the Bees feature selection algorithm is applied to select the desired number of features for final classification tasks. IKFDB is recorded with Kinect sensor V.2 and contains colour and depth images for facial and facial microexpression recognition purposes. Here, five facial expressions, Anger, Joy, Surprise, Disgust and Fear, are used for final validation. The proposed Bees LPQ method is compared with Particle Swarm Optimisation (PSO) LPQ, PCA LPQ, Lasso LPQ, and just LPQ features for classification tasks with Support Vector Machines (SVM), K-Nearest Neighbourhood (KNN), Shallow Neural Network and Ensemble Subspace KNN. The returned results show a promising performance of the proposed algorithm (99% accuracy) in comparison with others. -
Optimisation of Convolutional Neural Network Parameters Using the Bees Algorithm
Michael S. Packianather, Nawaf Mohammad H. AlamriThe chapter delves into the optimization of Convolutional Neural Network (CNN) parameters using the Bees Algorithm (BA), highlighting the challenges in CNN training such as overfitting and the exploding gradient problem. It introduces hybrid algorithms like BA-BO-CNN and BA-CNN, which combine Bayesian Optimization and BA to optimize CNN hyperparameters and weight learning rates. The chapter presents a thorough literature review and discusses the impact of integrating nature-inspired algorithms with DL networks. Experimental results demonstrate the superior performance of the proposed algorithms in terms of accuracy and computational efficiency, making this chapter a valuable resource for professionals seeking to enhance CNN topology and performance.AI Generated
This summary of the content was generated with the help of AI.
AbstractThe convolutional neural network (CNN) is one of the most popular deep learning algorithms that deals mainly with image data. In this study, the application of the Bees Algorithm (BA), which behaves like honeybees, was used along with the Bayesian Optimisation (BO) approach to improve CNN performance (BA-BO-CNN). Applying the hybrid algorithm on Cifar10DataDir images increased the accuracy on the validation set from 80.72% for the existing BO-CNN to 82.22% for the hybrid algorithm. Applying BA-BO-CNN on digit datasets showed the same accuracy as the existing hybrid BO-CNN, but with a computational time shortened by 3 min and 12 s. Finally, concrete crack benchmark image data yielded almost similar results to existing algorithms. Similarly, a new hybrid Bees Convolutional Neural Network (BA-CNN) algorithm was proposed that uses BA to design better CNN topology by optimising its hyperparameters, which increases the network accuracy. Applying the hybrid algorithm to the Cifar10DataDir dataset yielded the same accuracy as existing CNNs and BO-CNNs. Applying it to the digits dataset produced the lowest computational time with 4 min and 14 s reductions compared to BO-CNN, so it is the best algorithm in terms of cost-effectiveness. Finally, applying it to concrete crack images produced similar results to the existing algorithms. -
Ergonomic Risk Assessment Combining the Bees Algorithm and Simulation Tools
Abhijeet Singh, Mario Caterino, Marta Rinaldi, Marcello Fera, Roberto Macchiaroli, D. T. PhamThis chapter introduces a novel methodology combining the Bees Algorithm with simulation tools to assess and minimize ergonomic risks for heterogeneous workers in manufacturing and production lines. The study addresses the limitations of traditional observational methods by leveraging digital twins and simulation software to evaluate ergonomic risk factors objectively and efficiently. The Bees Algorithm is proposed as a powerful optimization tool to determine optimal job rotation strategies, ensuring minimal ergonomic scores for workers. The chapter validates the effectiveness of the Bees Algorithm through a case study, comparing its results with exact solutions obtained by enumeration. This approach promises to revolutionize ergonomic risk assessment, offering a more accurate and time-efficient solution for designing safe and productive work environments.AI Generated
This summary of the content was generated with the help of AI.
AbstractThis study proposes a methodology for integrating the use of the Bees Algorithm (BA) and simulation to reduce the workload for workers on production and assembly lines. Simulation is first employed for retrieving ergonomics data for different workers. Then, the BA is used as a tool for optimising the job rotations of the workers among the workstations, considering as objective the minimisation of the 2 indices, the Ovako Working Posture Analysing System (OWAS) and Energy Expenditure (EE). A case study related to a car assembly line is then proposed to test the methodology. Six workstations (WSs) are considered in this study, and the BA is validated by comparing its results with the exact solution evaluated by analysing all the possible combinations. The results show the good accuracy of the BA in finding solutions for this type of problem and the capacity to reduce the computational times compared with those of the exact solution. -
A Knowledge Transfer-Based Bees Algorithm for Expert Team Formation Problem in Internet Company
Yanjie Song, Yangyang Guo, Jiting Li, Jian Wu, Qinwen Yang, Yingwu ChenThe chapter addresses the complex team formation problem in Internet companies, where selecting the right experts is crucial for project success. It introduces a 0-1 programming model to minimize communication costs while meeting skill requirements. The bees algorithm, enhanced with a knowledge transfer mechanism, effectively explores the solution space and improves global search efficiency. The algorithm's performance is validated through extensive experiments, demonstrating its superiority over traditional methods. The chapter also highlights the impact of subpopulation size on algorithm performance, providing insights for practical applications in large-scale projects.AI Generated
This summary of the content was generated with the help of AI.
AbstractHow to build a team of experts plays an important role in the smooth development of a new project. The Internet company was investigated to determine how to form a team that can communicate effectively. This problem is known as the problem of expert team formation in Internet companies (TFPICs). We construct a mathematical optimisation model based on an undirected graph and propose a bees algorithm using knowledge transfer, named KT-BA. KT-BA uses the best information obtained through cooperative search of multiple subpopulations and transfers the dominant subpopulations to other subpopulations. A reproduction mechanism is also used in the algorithm to generate new individuals to enhance population diversity. The experimental results verify the solution performance of KT-BA, and the obtained expert team formation scheme outperforms the traditional bees algorithm and neighbourhood search algorithm. Thus, KT-BA is suitable for application in large Internet companies to provide scientific decisions for project development. -
Green Vehicle Routing Optimisation Using the Bees Algorithm
Aryan Satpathy, Millon Madhur Das, Natalia Hartono, D. T. PhamThe chapter delves into the critical issue of reducing greenhouse gas emissions from the transportation sector, with a focus on the Green Vehicle Routing Problem (GVRP). It introduces the Bees Algorithm as a novel approach to optimise vehicle routes, incorporating alternative fueling stations to ensure vehicles do not run out of fuel. The authors propose two local search operators, permuteAFS and removeAFS, to enhance the Bees Algorithm's efficiency in solving GVRP. The chapter provides a comprehensive literature review, outlines the methodology and research steps, presents and discusses the results, and concludes with recommendations for further research. This work is significant as it offers a practical solution to the complex problem of reducing emissions in the transportation sector, contributing to the global effort to reach net zero by 2050.AI Generated
This summary of the content was generated with the help of AI.
AbstractThe green vehicle routing problem (GVRP) aims to find a set of vehicle tours that minimise the total distance travelled to service a subset of customers while incorporating stops at alternative fuel stations because of the vehicle's limited fuel capacity. This is the first study to investigate using the Bees Algorithm to find a solution to the GVRP. The aim is to apply the Bees Algorithm to the GVRP and compare the results of six optimisation algorithms. A grid search was used to find the best parameters of the algorithms. For a fair comparison, the same number function of evaluation was used as the stopping criterion for all algorithms in this study. Statistical analysis is used to evaluate the performance of the algorithms. The results demonstrate that the Bees Algorithm outperforms other algorithms and can find near-optimal solutions. -
Utilising the Bees Algorithm for UAV Path Planning—A Simultaneous Collision Avoidance and Shortest Path Approach
Anubhab Dasgupta, Satyam Kumar, Aaditri Vaibhav, Asrul Harun IsmailThe chapter delves into the use of the Bees Algorithm (BA) for autonomous UAV path planning, specifically focusing on avoiding collisions and finding the shortest path. It begins by introducing the relevance of UAVs in various tasks and the challenges posed by combinatorial optimization problems. The Bees Algorithm is chosen for its effectiveness in solving such problems, with a particular focus on the BA 2 version developed by the authors. The chapter then outlines the problem formulation, including the UAV model, obstacles, and the cost function that balances fuel consumption and collision avoidance. The methodology section describes the implementation of BA 2 for UAV collision avoidance, comparing it with other metaheuristic algorithms. The results and analysis showcase the superior performance of BA 2 in terms of best cost, mean cost, and computational efficiency. Additionally, the chapter proposes a logarithmic distribution for recruitment, further enhancing the algorithm's performance. The conclusion highlights the potential for future work, including extending the study to 3D environments and multiple UAVs. This chapter offers a comprehensive and insightful exploration of the Bees Algorithm's application in UAV path planning, making it a valuable resource for professionals in the field.AI Generated
This summary of the content was generated with the help of AI.
AbstractDue to the significant interest in Unmanned Aerial Vehicles (UAVs), numerous metaheuristic or nature-inspired algorithms have been proposed for path-planning problems, allowing units to navigate through diverse threats effectively. By strategically avoiding threats identified by radar, these algorithms aim to determine the most optimal route. This chapter explores the application of two variants of the Bees Algorithm (BA) to address this issue and evaluates their performance against other metaheuristic algorithms. The comparison encompasses various configurations of waypoints and threats, with statistical analyses conducted to assess performance differences. The findings indicate that the Bees Algorithm with two parameters (BA2) surpasses the performance of the Basic BA and other metaheuristic algorithms. Statistical estimates derived from the data are utilised for comparisons, ensuring that optimisation parameters for all algorithms are selected unbiased and meaningfully. -
A Tabu-Based Bees Algorithm for Unmanned Aerial Vehicles in Maritime Search and Rescue Path Planning
Yangyang Guo, Yanjie Song, Jiting Li, Junwei Ou, Lining Xing, Yue ZhangThe chapter introduces a Tabu-based bees algorithm (TBBA) for optimizing UAV maritime search and rescue path planning, addressing the challenges of large-scale scenarios and complex constraints. The mathematical model developed for the UAV maritime search and rescue path planning (SARPP) problem aims to minimize overall execution time and balance task allocation among search and rescue centers. The TBBA algorithm incorporates a Tabu search strategy to prevent repeated searches and improve optimization efficiency. Simulation experiments demonstrate that TBBA outperforms genetic algorithms and neighborhood search algorithms in solving large-scale problems, highlighting its potential for practical applications in maritime search and rescue operations.AI Generated
This summary of the content was generated with the help of AI.
AbstractUnmanned aerial vehicles (UAVs) play a vital role in maritime search and rescue (SAR). The ability to find people overboard quickly is closely related to the flight path setting. We construct a mathematical programming model considering the task size of each SAR center and the overall search time as the objective function for the maritime search and rescue path planning (SARPP) problem. Then, a Tabu-based bees algorithm (TBBA) is proposed considering the complex problem space and constraints. The TBBA adopts the idea of the Tabu strategy to guide bees’ search and records the improvement of the individual structure of scout bees’ search to reduce the possibility of invalid search occurrence. An individual replacement strategy is also used in the algorithm to randomly generate a new site to replace the worst site when the search is unsatisfactory. The effectiveness of the proposed algorithm is verified by various experiments. TBBA can obtain a more ideal SAR solution than the genetic algorithm and neighbourhood search algorithm. -
Pedestrian-Aware Cyber-Physical Optimisation of Hybrid Propulsion Systems Using a Fuzzy Adaptive Cost Map and Bees Algorithm
Ji Li, Mingming Liu, Chongming Wang, Yingqi Gu, Quan Zhou, Chengqing Wen, D. T. Pham, Hongming XuThe chapter discusses the pressing issue of environmental pollution caused by transportation and introduces a pedestrian-aware cyber-physical optimisation technique for hybrid propulsion systems. It combines the Bees Algorithm with a fuzzy adaptive cost map to optimise power-split control parameters based on real-time pedestrian density data. The approach aims to reduce vehicle emissions and pollutants' impact on pedestrians, offering a novel solution in the field of automotive engineering. The method is evaluated through real-world driving cycles and compared with other optimisation algorithms, demonstrating superior performance and robustness. The chapter also explores the impact of communication quality on the system's performance, highlighting the importance of signal quality in distributed control systems for connected and automated vehicles.AI Generated
This summary of the content was generated with the help of AI.
AbstractElectrified propulsion systems are a promising way of reducing traffic-related pollution. Because of the characteristics of the exhaust systems of engine-assisted vehicles, it is possible that pedestrians in close proximity to vehicles may encounter situations with high enough concentrations of emissions to cause specific health effects. To decrease the impact of vehicle emissions and pollutants on surrounding pedestrians, this chapter presents a cyber-physical optimisation technique for the pedestrian-aware supervisory control strategy of hybrid propulsion systems. The technique is a combination of the Bees Algorithm and a fuzzy adaptive cost map to optimise the rule-based power-split parameters. It is capable of self-adjusting the intertarget weights of exhaust emissions and fuel with real-time pedestrian density information during the optimisations. To examine the robustness of the hybrid propulsion system optimised by the introduced technique, the effects of communication quality and bootstrap sampling techniques are studied. The results show that applying the developed fuzzy adaptive cost map can reduce emissions to nearby pedestrians by 14.42%. -
Surrogate Model-Assisted Bees Algorithm for Global Optimisation of Microwave Filters
Feiying Lan, Lu Qian, Marco Castellani, Yi Wang, D. T. Pham, Yongjing WangThe chapter delves into the critical role of microwave filters in modern wireless communication systems and the challenges faced in their design due to multimodal and time-consuming optimization processes. It introduces the surrogate model-assisted Bees Algorithm, which leverages Gaussian process regression to prescreen promising candidate solutions, thereby reducing the need for expensive full-wave electromagnetic simulations. The method is compared to standard optimization techniques on benchmark functions and a case study on a microwave dielectric filter, demonstrating its superior performance and efficiency. The chapter also provides a detailed methodology, including the introduction of the Bees Algorithm and Gaussian process regression, and discusses related work in the field of surrogate model-assisted optimization.AI Generated
This summary of the content was generated with the help of AI.
AbstractMicrowave filter optimisation is an important example of black-box optimisation, where the objective function is unknown and requires full-wave electromagnetic (EM) simulations. This problem is challenging and even computationally intractable for commonly used global optimisation techniques due to the multimodal and computationally expensive nature of its objective function. This chapter proposes the surrogate-model-assisted Bees Algorithm. Gaussian process regression is used to model the unknown objective function and prescreen promising candidates for expensive EM simulations. In this scheme, the Bees algorithm is used to perform a global search and intelligent sampling for surrogate modelling. This method was evaluated on 7 benchmark functions and compared with the standard Bees Algorithm. Mann‒Whitney U tests indicated the statistical significance of the results. A case study involving a microwave dielectric filter demonstrated the significant advantages of using the proposed method in terms of high-quality design and a reduced number of EM simulation-based evaluations.
-
-
Backmatter
- Title
- Intelligent Engineering Optimisation with the Bees Algorithm
- Editors
-
D. T. Pham
Natalia Hartono
- Copyright Year
- 2025
- Publisher
- Springer Nature Switzerland
- Electronic ISBN
- 978-3-031-64936-3
- Print ISBN
- 978-3-031-64935-6
- DOI
- https://doi.org/10.1007/978-3-031-64936-3
Accessibility information for this book is coming soon. We're working to make it available as quickly as possible. Thank you for your patience.
