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Solving human-robot collaborative mixed-model two-sided assembly line balancing using multi-objective discrete artificial bee colony algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 187卷

作者： Huang, Yuzhe Sheng, Buyun Luo, Ruiping Lu, Yingkang Fu, Gaocai Yin, Xiyan Wuhan Univ Technol Dept Mech & Elect Engn Wuhan Peoples R China Hubei Univ Technol Dept Mech Engn Wuhan Peoples R China

Human-robot collaboration (HRC) is an emerging technology that aims to optimize production efficiency and reduce ergonomic risks by enabling humans and collaborative robots to work together in a shared workspace. This paper investigates HRC mixed-model two-sided assembly line balancing problem, which is a challenging real-world problem in manufacturing systems. The problem is formulated using a mixed-integer programming model with the objectives of minimizing ergonomic risk, energy consumption, and cycle time. To solve the problem efficiently, a multi-objective discrete artificial bee colony algorithm with specialist bees (MDABCSB) is proposed. MDABCSB utilizes a new five-vector encoding and decoding scheme and incorporates two types of neighbor structures for each vector. Additionally, the algorithm introduces specialist bees to search for the optimal solution for each objective, combines a tabu list to help them jump out of local optima, and employs variable neighborhood descent to enhance their exploration capabilities. Furthermore, the algorithm adopts a new temperature-based food sources update mechanism to probabilistically update the food sources of employed bees to reinforce exploitation. By comparing the proposed algorithm with other algorithms, the results demonstrate that MDABCSB performs better in terms of solution performance.

关键词： Two-sided assembly line balancing Human -robot collaboration Collaborative robots artificial bee colony algorithm Ergonomic risk Energy consumption

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Intelligent Monitoring and Treatment System of Heavy Metal Contaminated Soil Based on artificial bee colony algorithm and Edge Computing Nanotechnology

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-15页

作者： Hu, Ke Li, Dongdong Cui, Xiaolei Hu, Donghua Chen, Junliang Zhuan, Shaopeng Chang, Hao Zhang, Yaping An, Tingting Zhang, Juqin First Geol Brigade Hebei Bur Geol & Mineral Resour Handan 056000 Peoples R China

The CGABC algorithm improves the convergence speed of the algorithm by crossing the solution generated by the neighborhood search of bees with the current global optimal solution. By selecting reasonable cross-operation coefficients to balance the algorithm's global optimization ability and local search ability, and adding random interference terms to increase population diversity. By optimizing the test function, it was verified that the performance of the CGABC algorithm is superior to the ABC algorithm and GABC algorithm. In the context of environmental remediation, particularly in the treatment of heavy metal-contaminated soil, optimizing treatment methods through advanced computational algorithms has become increasingly critical. This paper introduces an intelligent monitoring and treatment system based on the artificial bee colony (ABC) algorithm, incorporating edge computing and nanotechnology for the remediation of heavy metal-contaminated soils. To improve the efficiency and effectiveness of the system, we propose two enhanced algorithms: the Cooperative Global artificial bee colony (CGABC) and Chaotic Tabu Search artificial bee colony (CTABC) algorithms. Magnetic core iron trioxide was prepared by co-precipitation method, with an average particle size of 10-15 nm. Magnetic mesoporous nanoparticles Fe3O4 and SiO2 with uniform particle size and good dispersion were prepared using the Stober method. The specific surface area before calcination was 305.8 m2/g, and the pore size was 2.5 nm. The average particle size after calcination was around 200 nm. EDTA ferric oxide and silica were obtained by modification with N triacetate sodium salt. Fourier transform infrared spectroscopy showed successful EDTA modification with a specific surface area.

关键词： artificial bee colony algorithm Cross operation Adaptive chaotic taboo search Edge computing nanotechnology Heavy metal pollution

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A Q-learning based multi-strategy integrated artificial bee colony algorithm with application in unmanned vehicle path planning

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 236卷

作者： Ni, Xinrui Hu, Wei Fan, Qiaochu Cui, Yibing Qi, Chongkai Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China Delft Univ Technol Delft Inst Appl Math NL-2628 CD Delft Netherlands Beijing Jiaotong Univ Sch Math & Stat Beijing 100044 Peoples R China

artificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities among various meta-heuristic algorithms. However, its monotonous and one-dimensional search strategy limits its searching performance in the solving process. Thus, to address this issue, a Q-learning based multi-strategy integrated ABC algorithm (QMABC) is proposed. In the QMABC, multiple search strategies are proposed to utilize different individual experiences and search approaches for solution updates. Then, Q-learning is employed for strategy selection. In comparison to previous studies, this paper introduces more effective state and action configurations within the framework of Q-learning. To evaluate the performance of the QMABC, CEC 2017 benchmark functions are adopted to compare it to different meta-heuristic algorithms including ABC based and non-ABC based algorithms. Moreover, applications in path planning are implemented to further verify the effectiveness of the QMABC. Overall, it should be highlighted that the proposed QMABC demonstrates superiority in both numerical and practical experiments.

关键词： Meta-heuristic algorithm artificial bee colony algorithm Multi-strategy Q-learning framework Path planning

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Improved artificial bee colony algorithm based on two-dimensional queue structure for complex optimization problems

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ALEXANDRIA ENGINEERING JOURNAL 2024年 86卷 669-679页

作者： Pan, Xiuqin Wang, Yun Lu, Yong Sun, Na Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

In the face of increasingly complex optimization problems, the artificial bee colony algorithm still encounters some unresolved issues. For instance, the randomness involved in generating the initial population provides diversity to optimization solutions but also leads to convergence among individuals due to the current pseudo-randomness in this process. To address this challenge, this study introduces an improved ABC algorithm based on a two-dimensional queue structure, referred to as KLABC. The KLABC algorithm introduces several key advantages over traditional ABC algorithms. By employing the K-Means algorithm, it enhances the assessment of initial population individuals, ensuring a higher-quality population that aligns with specified requirements. The integration of a two-dimensional queue-based search strategy, along with the application of the first-in, first-out (FIFO) mechanism, not only preserves population diversity but also minimizes the risk of discarding potential optimal solutions during iterative searches. Augmenting this framework, the concepts of the global best solution (GBest) and queue best solution (QBest) are incorporated to fortify the exploration formula, expediting the algorithm's convergence rate. Experimental results on clustering optimization problems across seven UCI datasets validate the effectiveness and robustness of the proposed algorithm. Experimental results indicate that the KLABC algorithm achieves faster convergence compared to the traditional ABC algorithm, with an average performance improvement of 55.63%. Additionally, when comparing the runtime of different algorithms in handling large-scale data, the KLABC algorithm effectively maintains its performance even in scenarios with larger data sizes.

关键词： artificial bee colony algorithm Initial Population Optimization K-Means Global Best Solution Two-dimensional Queue Clustering Optimization UCI Datasets

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Multi-strategy-based artificial bee colony algorithm for AUV path planning with angle constraints

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OCEAN ENGINEERING 2024年第Part3期312卷

作者： Xu, Jian Han, Zhiyong Yin, Liangang Yan, Zheping Yu, Yuyang Ma, Guangzhi Harbin Engn Univ Harbin 150001 Peoples R China Harbin Engn Univ Qingdao Innovat & Dev Ctr Qingdao 266000 Peoples R China

This paper focuses on a path planning problem of an autonomous underwater vehicle (AUV) traversing target points at desired angles in an obstacle environment with eddy currents. A tangent-spatial partition method for path planning model construction is developed, which guarantees the direction of arrival in addition to extending the scope of waypoint search. An improved artificial bee colony algorithm integrating with multiple evolutionary strategies (ABC-MES) is proposed. More specifically, a population initialization method that follows the best point set principle is presented to increase population diversity and accelerate convergence. A multi-strategy evolutionary approach is developed in the employed bees phase to enhance population quality and establish a decision support database for subsequent upper confidence bound (UCB) learning. The UCB algorithm is then applied in the onlooker bee stage for assessment and screening of the optimal evolutionary strategy according to the accumulation of prior knowledge and the exploration of new knowledge. Finally, the T-distribution and reversal learning are explored in the scout bee stage to update old nectar sources to prevent the algorithm from falling into the local optimal prematurely. The strong ability of the proposed ABC-MES algorithm to jump out of the local optima is ensured by comparing it with 9 existing algorithms in terms of accuracy and stability with the help of 28 test functions. Simulation results reveal that the path generated by the proposed ABC-MES algorithm has lower overall cost accounting for time efficiency, navigation distance and energy consumption.

关键词： artificial bee colony algorithm Path planning Multi-strategy co-evolution Upper confidence bound algorithm

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Delivery Route Scheduling of Heterogeneous Robotic System with Customers Satisfaction by Using Multi-Objective artificial bee colony algorithm

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DRONES 2024年第10期8卷 519-519页

作者： Chen, Zhihuan Hou, Shangxuan Wang, Zuao Chen, Yang Hu, Mian Ikram, Rana Muhammad Adnan Wuhan Univ Sci & Technol Engn Res Ctr Met Automat & Measurement Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Inst Robot & Intelligent Syst Wuhan 430081 Peoples R China Guangzhou Univ Sch Architecture & Urban Planning Guangzhou 510006 Peoples R China

This study addresses the route scheduling problem for the heterogeneous robotic delivery system (HRDS) that perform delivery tasks in an urban environment. The HRDS comprises two distinct types of vehicles: an unmanned ground vehicle (UGV), which is constrained by road networks, and an unmanned aerial vehicle (UAV), which is capable of traversing terrain but has limitations in terms of energy and payload. The problem is formulated as an optimal route scheduling problem in a road network, where the goal is to find the route with minimum delivery cost and maximum customer satisfaction (CS) enabling the UAV to deliver packages to customers. We propose a new method of route scheduling based on an improved artificial bee colony algorithm (ABC) and the non-dominated sorting genetic algorithm II (NSGA-II) that provides the optimal delivery route. The effectiveness and superiority of the method we proposed are demonstrated by comparison in simulations. Moreover, the physical experiments further validate the practicality of the model and method.

关键词： heterogeneous robotic delivery system customer satisfaction route scheduling multi-objective optimization artificial bee colony algorithm

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A Comprehensive Analysis of PAPR Reduction in Scrambled UFMC and OFDM Using artificial bee colony algorithm for 5G Communications

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IETE JOURNAL OF RESEARCH 2024年第5期70卷 4355-4365页

作者： Kishore, K. Krishna Naveen, V. Jagan Kumar, P. Rajesh Rao, E. Sreenivasa Vasavi Coll Engn Dept ECE Hyderabad India GMR Inst Technol Dept ECE Rajam India Andhra Univ Dept ECE Visakhapatnam India

With the advent of 5G communications, the demand for high data rates and improved spectral efficiency has necessitated the exploration of advanced modulation techniques. Orthogonal Frequency Division Multiplexing (OFDM) and Universal Filtered Multi-Carrier (UFMC) are two prominent modulation schemes for 5G systems. However, both OFDM and UFMC suffer from a high Peak-to-Average Power Ratio (PAPR), leading to signal distortion and inefficient power amplification. The Partial Transmit Sequence (PTS) approach is a promising strategy to minimize PAPR in OFDM and is applied to UFMC to minimize PAPR in UFMC and is known as scrambled UFMC (S-UFMC). PTS, however, involves an extensive search to identify the best phase factors that increase computational complexity. The artificial bee colony algorithm is proposed to reduce the computational complexity S-UFMC and OFDM. A novel modulation scheme, amplitude phase shift keying (APSK), is used instead of quadrature phase shift keying (QPSK) and quadrature amplitude modulation (QAM) in S-UFMC and OFDM, which has better performance than conventional modulation schemes. In this research, OFDM, PTS OFDM, UFMC and S-UFMC are compared and it is found that the proposed scrambled UFMC with APSK modulation using ABC algorithm performs better than OFDM and UFMC in terms of PAPR, spectral efficiency and computational complexity.

关键词： Amplitude phase shift keying (APSK) artificial bee colony algorithm Computational complexity Dolphy Chebyshev filter Orthogonal frequency division multiplexing (OFDM) Partial transmit sequence (PTS) Peak to average power ratio (PAPR) Signal scrambling Spectral efficiency Universal filter multi-carrier (UFMC)

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Grouping and Reflection of the artificial bee colony algorithm for High-Dimensional Numerical Optimization Problems

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IEEE ACCESS 2024年 12卷 91426-91446页

作者： Phoemphon, Songyut Suranaree Univ Technol Inst Digital Arts & Sci DIGITECH Nakhon Ratchasima 30000 Thailand

The artificial bee colony (ABC) algorithm, inspired by the cooperative foraging behaviors observed in bees, is a prominent example of a swarm intelligence algorithm that offers significant advantages in optimization problems. However, the efficacy of the ABC algorithm is limited in high-dimensional scenarios or when handling multimodal functions, which contain many local optima because of the random nature of the one-dimensional search process for improving the position in the employed and onlooker bee phases. As a result, ABC has a limited ability to obtain the optimum result (slow convergence rate). To address this limitation, this research introduces Grouping and Reflection of the artificial bee colony (GRABC), a distinctive adaptation of the traditional ABC algorithm meticulously tailored to meet the specific demands of high-dimensional numerical optimization problems by balancing exploration and exploitation processes. GRABC strategically incorporates vector reflection and inertial weighting to formulate equations vleft and vright, which enhance both the employed and onlooker bee phases, substantially improving the convergence speed and improving the exploitation process. Moreover, the integration of grouping bees facilitates the exploration of food sources by promoting diversification and improving the exploration process. Additionally, an equation is derived to accurately compute the new positions of scout bees (exploration process), accounting for the possibility of becoming stuck in local optima and considering the proper limit values. The effectiveness of GRABC is thoroughly evaluated using 32 numerical benchmark functions, mostly including CEC 2017, which encompasses 100 dimensions. The empirical findings compellingly demonstrate that the GRABC algorithm outperforms alternative methodologies in terms of solution quality and convergence characteristics, as substantiated by comprehensive assessments that include metrics such as the worst, best, and average

关键词： Optimization methods artificial bee colony algorithm Convergence Reflection Particle swarm optimization Vectors Search problems Optimization problem swarm intelligence artificial bee colony grouping vector reflection

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Learning high-order fuzzy cognitive maps via multimodal artificial bee colony algorithm and nearest-better clustering: Applications on multivariate time series prediction

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KNOWLEDGE-BASED SYSTEMS 2024年 295卷

作者： Li, Zhuofan Liu, Xiaoqian Zhang, Yingjun Qin, Jiahu Zheng, Wei Xing Wang, Jingping Beijing Jiaotong Univ Sch Comp Sci & Technol Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Western Sydney Univ Sch Comp Data & Math Sci Sydney NSW 2751 Australia Beijing Normal Univ Ctr Informat & Network Technol Beijing 100875 Peoples R China

As an effective soft computing method, fuzzy cognitive maps (FCMs) have been successfully utilized to process time series prediction problems. However, FCM-based time series prediction models face some challenges including the complicated spatial-temporal dependencies, the complex causal relations among different variables, the low convergence speed, the immersing local minimization, and the non-convex optimization problems. To address these challenges, we propose a multivariate time series prediction model combining niching-based artificial bee colony algorithm and high-order fuzzy cognitive maps (HFCMs), termed NABCHFCM. Firstly, the learning of the HFCM is divided into multiple multimodal optimization problems (MMOPs). Secondly, a complete mathematical frame via multimodal artificial bee colony algorithm and nearest-better clustering is established to solve all decomposed MMOPs. Finally, the learned HFCM can be employed to predict the time series evolution trend. Experimental results on eight multi-variate datasets have demonstrated better prediction and generalization performance of NABC-HFCM by comparison with several representative baseline algorithms as a whole.

关键词： Multivariate time series prediction High-order fuzzy cognitive maps artificial bee colony algorithm Multimodal optimization

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Time optimization of urban arterial coordinated control based on multidimensional neighbourhood artificial bee colony algorithm

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TRANSPORTATION PLANNING AND TECHNOLOGY 2024年第1期47卷 113-145页

作者： He, Ruichun Zhao, Hongxing Ye, Zhongni Yang, Liumeng Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou Peoples R China Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou Peoples R China Box 106 88 Anning West Rd Lanzhou Gansu Peoples R China

Considering the traffic flow of the inner and outer intersection approach of urban arterial roads, an optimization control model of traffic arterial road signal parameters with signal cycle, green light time, and phase offset as the optimization variables is proposed. In order to solve the optimization model of timing parameters, the swarm evolution mechanism of artificial bee colony algorithm is improved and applied. The case study shows that the optimization model of timing parameters can collaboratively optimize the signal cycle, green light time and phase offset of the arterial coordinated control system, and the timing results of the optimization model by comprehensive consideration of inner and outer approach traffic flow are more beneficial to the improvement of traffic efficiency than the decoupling optimization algorithm. The proposed optimization model and solution algorithm can improve the effect of arterial coordinated control.

关键词： Traffic arterial coordinated control signal timing optimization artificial bee colony algorithm

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