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Target link protection against link-prediction-based attacks via artificial bee colony algorithm based on random walk

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第11期15卷 4959-4971页

作者： Jiang, Zhongyuan Liu, Haibo Li, Jing Li, Xinghua Ma, Jianfeng Yu, Philip S. Xidian Univ Sch Cyber Engn Xian 710071 Shaanxi Peoples R China Univ Illinois Dept Comp Sci Chicago IL 60607 USA

Link prediction is a network analysis model used to discover missing links or future relationships that may appear, which has been widely used in many real network systems to predict the potential relationship between two individuals. However, link prediction can also be used by attackers to identify sensitive links that users are unwilling to expose, which makes only removing sensitive links from the original network ineffective and leads to the disclosure of privacy. In this paper, we propose a target link protection mechanism via artificial bee colony algorithm based on random walk (RABC), which can defend the link prediction attacks based on resource allocation (RA) metric effectively. To enhance the local search ability of RABC, the random walk algorithm is combined with the original artificial bee colony algorithm. Then, we compare our method with other existing methods, which shows that RABC has higher efficiency while ensuring the effectiveness. Finally, extensive experiments on real social networks are conducted to demonstrate the good performance of RABC on protecting sensitive links from being detected successfully by link prediction model. Furthermore, the perturbed networks generated by RABC is transferable to defend against other link prediction attacks.

关键词： Social networks Target link protection Privacy threats artificial bee colony algorithm Random walk

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Multi-robot path planning using learning-based artificial bee colony algorithm

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ENGINEERING APPLICATIONS OF artificial INTELLIGENCE 2024年 129卷

作者： Cui, Yibing Hu, Wei Rahmani, Ahmed Beijing Jiaotong Univ Sch Math & Stat Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China CRIStAL UMR CNRS 9189 Cent lille F-59651 Villeneuve Dascq France

Multi-robot path planning (MRPP) in continuous and known environment is studied in this paper via proposing a novel local path planning approach. To plan optimal collision-free paths for multiple robots simultaneously, a novel implementation method suitable for the meta-heuristic algorithms is devised, and an improved artificial bee colony (ABC) algorithm is developed. Three enhancements to the ABC algorithm are made in this context. Firstly, to better lead the search direction, the global best individual is involved in the search equations of employed bee phase and scout bee phase. Meanwhile, to boost exploitation capability, the learning method of teaching-learning based optimization (TLBO) algorithm is incorporated into the onlooker bee phase. The proposed learning-based ABC (ABCL) algorithm is used to determine the subsequent positions for all the robots based on their current coordinates considering the path length, safety and planning efficiency. The experimental studies on benchmark functions show that ABCL is outstanding in solving different types of optimization problems compared against seven effective meta-heuristic algorithms. More importantly, MRPP simulation results prove that ABCL outperforms its competitors in terms of generating optimal collision-free paths and running time. Compared with the original ABC, ABCL improves these two aspects on average for all tasks by 2.1% and 12.6%, respectively. Therefore, with the proposed implementation method, ABCL can be considered as an effective MRPP solution.

关键词： Multi-robot path planning Obstacle avoidance artificial bee colony algorithm Teaching-learning based optimization algorithm

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A hierarchical learning based artificial bee colony algorithm for numerical global optimization and its applications

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APPLIED INTELLIGENCE 2024年第1期54卷 169-200页

作者： Zhang, Qingke Bu, Xianglong Gao, Hao Li, Tianqi Zhang, Huaxiang Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China

The artificial bee colony algorithm (ABC) is a swarm intelligence algorithm inspired by honey bee harvesting behavior. It boasts the benefits of minimal parameters and strong exploration capabilities. However, the ABC algorithm is still susceptible to local optima entrapment and lacks consideration of selection probability in the onlooker bee phase, leading to reduced convergence accuracy in later search stages. To address these issues, this paper introduces an enhanced ABC algorithm called Hierarchical Learning-based artificial bee colony (HLABC). Initially, a hierarchical learning approach is devised, dividing the entire population into distinct layers based on solution quality. In this hierarchical approach, bees at lower layers can access much better advantageous information from higher layers. Secondly, the exploitation ability of onlooker bees is enhanced through novel strategies designed based on hierarchical learning. Thirdly, the exploration ability of scout bees is strengthened by implementing an opposition-based learning method. To evaluate the performance of the proposed algorithm, 69 benchmark functions from four benchmark suites (CEC2005, CEC2010, CEC2013 and CEC2022) are used to test the performance of HLABC, along with five variants of the ABC algorithm, The experimental statistical results show that the HLABC algorithm outperforms the ABC algorithm on all test problems with an average winning rate of 89%. Furthermore, to validate the performance of the HLABC algorithm in real-world optimization problems, this paper applies the HLABC algorithm to two practical applications: the deployment of wireless sensor networks (WSNs), the power scheduling problem in a smart home (PSPSH) and the multi-thresholding image segmentation (MIS). The experimental and statistical results demonstrate that HLABC is an efficient and stable optimizer. It shows better or comparable performance compared to other ABC variants when considering the quality of solutions for a sui

关键词： artificial bee colony algorithm Hierarchical learning Numerical global optimization Wireless sensor networks deployment Power scheduling problem Multi-thresholding image segmentation

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Solving line balancing and AGV scheduling problems for intelligent decisions using a Genetic-artificial bee colony algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 189卷

作者： Mumtaz, Jabir Minhas, Kaynat Afzal Rauf, Mudassar Yue, Lei Chen, Yarong Wenzhou Univ Coll Mech & Elect Engn Wenzhou Peoples R China Univ Engn & Technol Ind Engn Dept Taxila Pakistan Guangzhou Univ Sch Mech & Elect Engn Guangzhou Peoples R China

Due to the rapid advancement of technology, the demand for electronic devices in various sectors such as consumer electronics, automotive, telecommunications, healthcare, and industrial applications, as well as customized printed circuit board (PCB) products, has significantly increased. Balancing the PCB assembly line is crucial for improving productivity to meet market demand, which means balancing the workload distributed among various assembly stations on the assembly line. However, the workload balancing depends on materialhandling systems in production environments that facilitate the transportation of raw materials to the buffer storage of the assembly stations. In the PCB assembly system, the material handling is carried out using automated guided vehicles (AGVs). This paper studies the assembly line balancing and AGV scheduling collectively because of their dependency on each other. A mixed integer linear programming (MILP) model is formulated to balance the workload distribution among stations and schedule the AGVs. An existing intelligent platform of the real-life PCB industry is considered to solve this integrated problem with intelligent decisions. The platform includes three layers modules: physical layer, data management layer and application service layer. A novel genetic artificial bee colony (GABC) algorithm is proposed and embedded with an application service layer to optimize the current problem and give optimum solutions for the real-life physical layer. The proposed GABC algorithm incorporates the operators of the genetic algorithm (GA) i.e., crossover and mutation into the search process of the ABC algorithm. Additionally, the greedy selection feature is employed in GABC which significantly enhances the exploitation capabilities, leading to faster convergence, higher-quality solutions, and improved robustness. The performance of the proposed GABC algorithm is tested based on different parameters by comparing the results with GA, ABC and PSO al

关键词： Assembly line balancing AGV scheduling Workload Smart decision-making artificial bee colony algorithm

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Remanufacturing system scheduling of batch products with the consideration of dynamic changes in machine efficiency using an improved artificial bee colony algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 187卷

作者： Jin, Qinyu Zhang, Shuai Xu, Jiyuan Jiang, Kang Cheng, Yuanzhe Zhang, Wenyu Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310018 Peoples R China Zhejiang Univ Finance & Econ Sch Yingyang Financial Technol Hangzhou 310018 Peoples R China

Remanufacturing has the ability to bring the quality and condition of the recycled product back to the level of or even better than the new product. As remanufacturing continues to receive more and more attention from industry and academia, the scheduling of remanufacturing systems (RMS) has been extensively studied. However, few studies have not only focused on the dynamic changes (deterioration and learning) in machine efficiency during RMS scheduling but also considered that the three subsystems (disassembly, reprocessing and reassembly) of RMS collaborate with each other during scheduling. Therefore, this study proposes a new RMS scheduling model of batch recycled products with the consideration of dynamic changes in machine efficiency for handling the batch recycled products. It considers not only the collaboration among the three subsystems comprehensively, but also the dynamic changes in machine efficiency, i.e., deterioration effects and learning effects, during the scheduling. To solve this model, an improved artificial bee colony algorithm with a new representation scheme and a new population generating method is proposed. The effectiveness and practicality of this algorithm are examined through simulation experiments and comparison with other baseline algorithms.

关键词： Remanufacturing system RMS subsystems Scheduling Deterioration effects Learning effects artificial bee colony algorithm

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Predictive modeling of combustion cycle variations in spark ignition engine based on backpropagation neural network and artificial bee colony algorithm

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ENGINEERING APPLICATIONS OF artificial INTELLIGENCE 2025年 152卷

作者： Pan, Mingzhang Pan, Yuede Fu, Changcheng Cao, Xinxin Zhang, Song Yang, Jun Sang, Hailang Guan, Wei Guangxi Univ Coll Mech Engn Nanning 530004 Peoples R China Guangxi Yuchai Machinery Co Ltd Tech Res Inst Nanning Peoples R China

This study proposes a back-propagation neural network (BPNN) method optimized by the artificial bee colony (ABC) algorithm for predicting cyclic variations in an ignition engine. The proposed method integrates a meta-heuristic (MH) algorithm with deep learning techniques to develop a high-precision prediction model. This model utilizes engine speed, exhaust gas recirculation (EGR) ratio, and excess air ratio (lambda) as input parameters, with indicated mean effective pressure (IMEP) as the output parameter. A multi-stage optimization strategy is employed in this study. Initially, the optimal structural parameters of the BPNN are determined through experimental design, including 90 and 84 neurons in the two hidden layers, the Sigmoid activation function, and a learning rate of 0.01. Subsequently, the ABC algorithm is utilized to globally optimize the initial weights and biases of the BPNN, thereby mitigating the tendency of the BPNN to converge to local optima. Experimental results demonstrate that the optimized ABC-BPNN model exhibits exceptional prediction performance, achieving a coefficient of determination (R2) of 0.9407, a root mean square error (RMSE) of 0.3673, and a mean absolute error (MAE) of 0.2986. Furthermore, the ABC-BPNN model exhibits superior prediction capability for IMEP when compared to other advanced algorithms. Lastly, analysis of the experimental results shows that an increase in the excess air ratio and EGR ratio aggravates combustion instability. Consequently, the novelty of this study is to improve the accuracy and reliability of engine combustion process prediction and to provide a new path for performance optimization of complex industrial systems.

关键词： Cyclic variations Back propagation neural network artificial bee colony algorithm Indicated mean effective pressure

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Optimal vegetable selection in urban and rural areas using artificial bee colony algorithm: Heavy metal assessment and health risk

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2025年 139卷

作者： Gueltekin, Yucel Bayraktar, Mukaddes Kilic Sevik, Hakan Cetin, Mehmet Bayraktar, Tugrul Inst Nat Sci Grad Sch Nat & Appl Sci Dept Sustainable Agr & Nat Plant Resources TR-37150 Kastamonu Turkiye Karabuk Univ Fac Hlth Sci Dept Nutr & Dietet TR-78000 Karabuk Turkiye Kastamonu Univ Fac Engn & Architecture Dept Environm Engn TR-37150 Kastamonu Turkiye Ondokuz Mayis Univ Fac Architecture Dept City & Reg Planning TR-55420 Samsun Turkiye Karabuk Univ Fac Engn Dept Ind Engn TR-78000 Karabuk Turkiye Karabuk Univ Fac Hlth Sci Dept Nutr & Dietet TR-78050 Karabuk Turkiye

Industrial and traffic activities have raised heavy metal (HM) pollution, increasing health risks from contaminated vegetables. The study aims to analyze HM concentrations of lead (Pb), iron (Fe), and aluminum (Al) in Solanum lycopersicum L. (tomato), Capsicum annuum L. (pepper), Phaseolus vulgaris L. (bean), and Zea mays L. (corn) plants grown in urban and rural areas of Ordu province, T & uuml;rkiye. Variations in the HMs were evaluated based on species, organ, growing area, and washing status. The goal is to use the artificial bee colony (ABC) algorithm to identify the best vegetable combination based on health risk assessment. Tomato and corn had the lowest HM levels, while pepper had the highest. Urban vegetables had high Pb levels, with urban-grown corn showing notably high Fe and Al levels. Pb levels (341.4-13,240.4 mu g/kg) exceeded permissible limits in all vegetables, Al (898.9-210,706.2 mu g/kg) in most, while Fe (11.2-298.4 mu g/kg) stayed within safe limits. Health risk assessments (hazard quotient and hazard indices <1) show no risk of non-carcinogenic diseases. The recommended upper limits for HM concentrations constrain vegetable choices to minimize health risks, with the ABC algorithm advising washed pepper, tomato, and bean from urban areas and unwashed corn from rural areas.

关键词： Heavy metal accumulation Traffic density Vegetables Food safety Health risk assessment artificial bee colony algorithm

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Modelling of optimal transportation route selection based on artificial bee colony algorithm

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2024年第1-2期46卷 90-111页

作者： Liu, Honggang Zhang, Peilin Wu, Hua Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan Hubei Peoples R China Air Force Logist Univ Dept Air Transport & Delivery Guarantee Xuzhou Jiangsu Peoples R China

Optimising enterprise management and reducing logistics costs have become the common focus of Chinese logistics companies. This study mainly discusses the modelling of optimal transportation route selection based on artificial bee colony algorithm. In this paper, the designed bee colony algorithm is used to solve the cross-dock vehicle scheduling part in the mathematical model, and the cross-dock gate allocation and vehicle parking sequence scheduling schemes are obtained. Then, the scheduling scheme is used as the known condition of the path optimisation part, and the vehicle transportation path is optimised by using the bee colony algorithm, and the total cost is minimised in the process of mutual iteration. The research results show that the proportion of leading bees is 70% when the best calculated average value is obtained, and 90% when the variance of the calculated results is the smallest.

关键词： artificial bee colony algorithm transportation mode optimal path selection modelling

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Energy consumption calculation and energy-saving measures of substation based on Multi-objective artificial bee colony algorithm

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2024年第1期25卷 25-34页

作者： Zhao, Na Wang, Jicheng Ding, Yuxian Li, Yifeng Inner Mongolia Univ Sci & Technol Coll Civil Engn Baotou 014000 Peoples R China State Grid East Inner Mongolia Elect Power Supply Hohhot 010000 Mongolia Peoples R China Inner Mongolia Autonomous Reg Bldg Struct Disaster Baotou 014000 Mongolia Peoples R China Heilongjiang Huadian Qiqihar Thermal Power Co Qiqihar 161000 Heilongjiang Peoples R China

Electric energy is the most commonly used energy in people's daily life. In the process of use, how to improve the utilization rate of resources, so as to achieve the purpose of energy saving and environmental protection of power resources is a very significant work. The power grid is crucial in the transmission of electricity, and the energy consumption of substations is a major factor affecting the operation cost. Therefore, it is a key condition to ensure the stable operation of the power grid to scientifically calculate its energy consumption and formulate appropriate energy-saving measures. After consulting a large number of relevant documents and data, it has been found that many researchers have provided new ideas for the calculation of energy consumption and the study of energy-saving measures in substations. This article was adding its own understanding and taking it as the direction and basis. In the introduction, the research significance of substation energy consumption calculation was introduced, and then the academic research and analysis were carried out on the two key sentences of substation energy consumption calculation and analysis and the research of multi-objective artificial bee colony (ABC) algorithm in substation. In the method part, a constructive algorithm theory was proposed, and the application of the algorithm in substation energy consumption was proposed to provide a theoretical basis for the energy consumption calculation and energy conservation measures research of substations based on multi-objective ABC algorithm. At the end of the article, a simulation comparison experiment was carried out and the experiment was summarized and discussed. Firstly, the performance of the two algorithms was analyzed. Then, through the first selection of power plant T and Y, it was concluded that the resistivity of the cable in the distribution room in power plant Y was much higher than the average value based on the experimental results. Therefore, it

关键词： artificial bee colony algorithm effective energy efficiency energy consumption calculation energy measures substation energy consumption

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artificial bee colony algorithm using permutation encoding for the bounded diameter minimum spanning tree problem

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SOFT COMPUTING 2021年第16期25卷 11289-11305页

作者： Singh, Kavita Sundar, Shyam Natl Inst Technol Raipur Dept Comp Applicat Raipur 492010 Madhya Pradesh India

The bounded diameter minimum spanning tree (BD-MST) problem seeks a spanning tree (T) of minimum weight on a given connected, undirected and edge-weighted graph subject to the diameter of T does not exceed D >= 2, where D is a given positive integer. The BD-MST problem isNP-hard problem and finds many real-world applications. In this paper, we propose an artificial bee colony (ABC) algorithm for the BD-MST problem. ABC algorithm is a swarm-based metaheuristic technique based on the intelligent foraging behavior of honeybees. The proposed ABC algorithm employs permutation encoding. To exploit this encoding structure, two neighborhood strategies that help ABC algorithm in faster convergence towards finding high quality solutions are applied. On a set of Euclidean and non-Euclidean benchmark instances for various diameter bounds, the proposed approach has been compared with state-of-the-art approaches. Computational results demonstrate the effectiveness of the proposed approach to the other extant approaches in the literature.

关键词： Spanning tree Bounded-diameter Swarm intelligence artificial bee colony algorithm Permutation encoding Neighborhood strategies

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