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Journal articles on the topic 'Cooperative artificial bee colony'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Zou, Wenping, Yunlong Zhu, Hanning Chen, and Xin Sui. "A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm." Discrete Dynamics in Nature and Society 2010 (2010): 1–16. http://dx.doi.org/10.1155/2010/459796.

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Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by A

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2

Li, Jun-yi, Yi-ding Zhao, Jian-hua Li, and Xiao-jun Liu. "Artificial Bee Colony Optimizer with Bee-to-Bee Communication and Multipopulation Coevolution for Multilevel Threshold Image Segmentation." Mathematical Problems in Engineering 2015 (2015): 1–23. http://dx.doi.org/10.1155/2015/272947.

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This paper proposes a modified artificial bee colony optimizer (MABC) by combining bee-to-bee communication pattern and multipopulation cooperative mechanism. In the bee-to-bee communication model, with the enhanced information exchange strategy, individuals can share more information from the elites through the Von Neumann topology. With the multipopulation cooperative mechanism, the hierarchical colony with different topologies can be structured, which can maintain diversity of the whole community. The experimental results on comparing the MABC to several successful EA and SI algorithms on a

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3

Liang, Jun-Hao, and Ching-Hung Lee. "A Modification Artificial Bee Colony Algorithm for Optimization Problems." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/581391.

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This paper presents a modified artificial bee colony algorithm (MABC) for solving function optimization problems and control of mobile robot system. Several strategies are adopted to enhance the performance and reduce the computational effort of traditional artificial bee colony algorithm, such as elite, solution sharing, instant update, cooperative strategy, and population manager. The elite individuals are selected as onlooker bees for preserving good evolution, and, then, onlooker bees, employed bees, and scout bees are operated. The solution sharing strategy provides a proper direction for

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4

Lin, Chun-Hui, Shyh-Hau Wang, and Cheng-Jian Lin. "Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments." Sensors 18, no. 12 (2018): 4181. http://dx.doi.org/10.3390/s18124181.

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In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. Additionally, an interval type-2 neural fuzzy controller based on dynamic group artificial bee colony (DGABC) is proposed in this paper. Reinforcement learning was used to develop the WFM adaptively. First, a single robot is trained to learn the WFM. Then, this control method is implem

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Ye, Fang, Fei Che, and Lipeng Gao. "Multiobjective Cognitive Cooperative Jamming Decision-Making Method Based on Tabu Search-Artificial Bee Colony Algorithm." International Journal of Aerospace Engineering 2018 (December 9, 2018): 1–10. http://dx.doi.org/10.1155/2018/7490895.

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For the future information confrontation, a single jamming mode is not effective due to the complex electromagnetic environment. Selecting the appropriate jamming decision to coordinately allocate the jamming resources is the development direction of the electronic countermeasures. Most of the existing studies about jamming decision only pay attention to the jamming benefits, while ignoring the jamming cost. In addition, the conventional artificial bee colony algorithm takes too many iterations, and the improved ant colony (IAC) algorithm is easy to fall into the local optimal solution. Agains

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6

Harfouchi, Fatima, and Hacene Habbi. "A cooperative learning artificial bee colony algorithm with multiple search mechanisms." International Journal of Hybrid Intelligent Systems 13, no. 2 (2016): 113–24. http://dx.doi.org/10.3233/his-160229.

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7

Liang, Xiaodan, Lin Na, Liangyu Li, and Hanning Chen. "A Cooperative Coevolutionary Artificial Bee Colony Algorithm for Multi-Objective Optimization." Journal of Computational and Theoretical Nanoscience 13, no. 9 (2016): 6258–66. http://dx.doi.org/10.1166/jctn.2016.5555.

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8

Ma, Lianbo, Kunyuan Hu, Yunlong Zhu, and Hanning Chen. "Cooperative artificial bee colony algorithm for multi-objective RFID network planning." Journal of Network and Computer Applications 42 (June 2014): 143–62. http://dx.doi.org/10.1016/j.jnca.2014.02.012.

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9

Zhou, Xiaoming, Anlong Su, Aimin Liu, Wanli Cui, and Wei Liu. "Cooperative approach to artificial bee colony algorithm for optimal power flow." Cluster Computing 22, S4 (2018): 8059–67. http://dx.doi.org/10.1007/s10586-017-1594-9.

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10

Hei, Yongqiang, Wentao Li, Weihong Fu, and Xiaohui Li. "Efficient Parallel Artificial Bee Colony Algorithm for Cooperative Spectrum Sensing Optimization." Circuits, Systems, and Signal Processing 34, no. 11 (2015): 3611–29. http://dx.doi.org/10.1007/s00034-015-0028-2.

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11

Wang, Haiquan, Menghao Su, Xiaobin Xu, et al. "Cargo Terminal Intelligent-Scheduling Strategies Based on Improved Bee Colony Algorithms." Applied Sciences 13, no. 15 (2023): 8750. http://dx.doi.org/10.3390/app13158750.

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Due to the rapid increase in cargoes and postal transport volumes in smart transportation systems, an efficient automated multidimensional terminal with autonomous elevating transfer vehicles (ETVs) should be established, and an effective cooperative scheduling strategy for vehicles needs to be designed for improving cargo handling efficiency. In this paper, as one of the most effective artificial intelligence technologies, the artificial bee colony algorithm (ABC), which possesses a strong global optimization ability and fewer parameters, is firstly introduced to simultaneously manage the aut

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12

Li, Xinbin, Lu Lu, Lei Liu, Guoqiang Li, and Xinping Guan. "Cooperative spectrum sensing based on an efficient adaptive artificial bee colony algorithm." Soft Computing 19, no. 3 (2014): 597–607. http://dx.doi.org/10.1007/s00500-014-1280-2.

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13

Li, Min, Yongqiang Hei, and Zhuo Qiu. "Optimization of multiband cooperative spectrum sensing with modified artificial bee colony algorithm." Applied Soft Computing 57 (August 2017): 751–59. http://dx.doi.org/10.1016/j.asoc.2017.03.027.

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14

Hu, Chunyu, Peng Zhang, and Hong Liu. "Cooperative Co-evolutionary Artificial Bee Colony Algorithm Based on Hierarchical Communication Model." Chinese Journal of Electronics 25, no. 3 (2016): 570–76. http://dx.doi.org/10.1049/cje.2016.05.025.

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15

Celik, Mete, Fehim Koylu, and Dervis Karaboga. "CoABCMiner: An Algorithm for Cooperative Rule Classification System Based on Artificial Bee Colony." International Journal on Artificial Intelligence Tools 25, no. 01 (2016): 1550028. http://dx.doi.org/10.1142/s0218213015500281.

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In data mining, classification rule learning extracts the knowledge in the representation of IF_THEN rule which is comprehensive and readable. It is a challenging problem due to the complexity of data sets. Various meta-heuristic machine learning algorithms are proposed for rule learning. Cooperative rule learning is the discovery process of all classification rules with a single run concurrently. In this paper, a novel cooperative rule learning algorithm, called CoABCMiner, based on Artificial Bee Colony is introduced. The proposed algorithm handles the training data set and discovers the cla

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16

Jayalakshmi, B., and Alok Singh. "A hybrid artificial bee colony algorithm for the cooperative maximum covering location problem." International Journal of Machine Learning and Cybernetics 8, no. 2 (2015): 691–97. http://dx.doi.org/10.1007/s13042-015-0466-y.

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17

Zhang, Liming, Saisai Wang, Kai Zhang, et al. "Cooperative Artificial Bee Colony Algorithm With Multiple Populations for Interval Multiobjective Optimization Problems." IEEE Transactions on Fuzzy Systems 27, no. 5 (2019): 1052–65. http://dx.doi.org/10.1109/tfuzz.2018.2872125.

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18

Ren, Yaping, Guangdong Tian, Fu Zhao, Daoyuan Yu, and Chaoyong Zhang. "Selective cooperative disassembly planning based on multi-objective discrete artificial bee colony algorithm." Engineering Applications of Artificial Intelligence 64 (September 2017): 415–31. http://dx.doi.org/10.1016/j.engappai.2017.06.025.

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19

Aslan, Selcuk. "Deployment in wireless sensor networks by parallel and cooperative parallel artificial bee colony algorithms." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 9, no. 1 (2018): 1–10. http://dx.doi.org/10.11121/ijocta.01.2019.00576.

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Increasing number of cores in a processor chip and decreasing cost of distributed memory based system setup have led to emerge of a new work theme in which the main concern focused on the parallelization of the well-known algorithmic approaches for utilizing the computational power of the current architectures. In this study, the performances of the conventional parallel and cooperative model based parallel Artificial Bee Colony (ABC) algorithms on the deployment problem related to the wireless sensor networks were investigated. The results obtained from the experimental studies showed that pa

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20

He, Maowei, Kunyuan Hu, Yunlong Zhu, Lianbo Ma, Hanning Chen, and Yan Song. "Hierarchical Artificial Bee Colony Optimizer with Divide-and-Conquer and Crossover for Multilevel Threshold Image Segmentation." Discrete Dynamics in Nature and Society 2014 (2014): 1–22. http://dx.doi.org/10.1155/2014/941534.

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This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), for multilevel threshold image segmentation, which employs a pool of optimal foraging strategies to extend the classical artificial bee colony framework to a cooperative and hierarchical fashion. In the proposed hierarchical model, the higher-level species incorporates the enhanced information exchange mechanism based on crossover operator to enhance the global search ability between species. In the bottom level, with the divide-and-conquer approach, each subpopulation runs the o

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21

Zhao, Baohua, Tien-Wen Sung, and Xin Zhang. "A quasi-affine transformation artificial bee colony algorithm for global optimization." Journal of Intelligent & Fuzzy Systems 40, no. 3 (2021): 5527–44. http://dx.doi.org/10.3233/jifs-202712.

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The artificial bee colony (ABC) algorithm is one of the classical bioinspired swarm-based intelligence algorithms that has strong search ability, because of its special search mechanism, but its development ability is slightly insufficient and its convergence speed is slow. In view of its weak development ability and slow convergence speed, this paper proposes the QABC algorithm in which a new search equation is based on the idea of quasi-affine transformation, which greatly improves the cooperative ability between particles and enhances its exploitability. During the process of location updat

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22

Liu, Zhen, and Yun An Hu. "Quantum Artificial Bee Colony Algorithm for Knapsack Problem." Advanced Materials Research 605-607 (December 2012): 1722–28. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.1722.

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The traditional quantum evolutionary algorithm takes a long time to converge and can be easy trap into local optima. In order to overcome and accelerate the speed of the convergence, a new quantum evolutionary algorithm is proposed in the paper. The proposed new algorithm named discrete quantum bee colony algorithm incorporate the basic idea of the artificial bee colony algorithm. The initial population can be initialized randomly using quantum encoded and the population can be formed by there parts and every subpopulation can evolve cooperatively. In the end, the individual will rated accordi

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23

Gorji-Bandpy, M., and A. Mozaffari. "Multiobjective Optimization of Irreversible Thermal Engine Using Mutable Smart Bee Algorithm." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/652391.

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A new method called mutable smart bee (MSB) algorithm proposed for cooperative optimizing of the maximum power output (MPO) and minimum entropy generation (MEG) of an Atkinson cycle as a multiobjective, multi-modal mechanical problem. This method utilizes mutable smart bee instead of classical bees. The results have been checked with some of the most common optimizing algorithms like Karaboga’s original artificial bee colony, bees algorithm (BA), improved particle swarm optimization (IPSO), Lukasik firefly algorithm (LFFA), and self-adaptive penalty function genetic algorithm (SAPF-GA). Accord

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24

Harfouchi, F., H. Habbi, C. Ozturk, and D. Karaboga. "Modified multiple search cooperative foraging strategy for improved artificial bee colony optimization with robustness analysis." Soft Computing 22, no. 19 (2017): 6371–94. http://dx.doi.org/10.1007/s00500-017-2689-1.

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25

Thirugnanasambandam, Kalaipriyan, Rajakumar Ramalingam, Divya Mohan, Mamoon Rashid, Kapil Juneja, and Sultan S. Alshamrani. "Patron–Prophet Artificial Bee Colony Approach for Solving Numerical Continuous Optimization Problems." Axioms 11, no. 10 (2022): 523. http://dx.doi.org/10.3390/axioms11100523.

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The swarm-based Artificial Bee Colony (ABC) algorithm has a significant range of applications and is competent, compared to other algorithms, regarding many optimization problems. However, the ABC’s performance in higher-dimension situations towards global optima is not on par with other models due to its deficiency in balancing intensification and diversification. In this research, two different strategies are applied for the improvement of the search capability of the ABC in a multimodal search space. In the ABC, the first strategy, Patron–Prophet, is assessed in the scout bee phase to incor

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26

Li, Jianjun, and Ru Bo Zhang. "Multi-Auv Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm." Polish Maritime Research 24, s3 (2017): 65–71. http://dx.doi.org/10.1515/pomr-2017-0106.

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Abstract The multi-autonomous underwater vehicle (AUV) distributed task allocation model of a contract net, which introduces an equilibrium coefficient, has been established to solve the multi-AUV distributed task allocation problem. A differential evolution quantum artificial bee colony (DEQABC) optimization algorithm is proposed to solve the multi-AUV optimal task allocation scheme. The algorithm is based on the quantum artificial bee colony algorithm, and it takes advantage of the characteristics of the differential evolution algorithm. This algorithm can remember the individual optimal sol

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27

Bektur, Gulcin, and Hatice Kübra Aslan. "Artificial bee colony algorithm for operating room scheduling problem with dedicated/flexible resources and cooperative operations." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 14, no. 3 (2024): 193–207. http://dx.doi.org/10.11121/ijocta.1466.

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In this study operating room scheduling (ORS) problem is addressed in multi-resource manner. In the addressed problem, besides operating rooms (ORs) and surgeons, the anesthesia team is also considered as an additional resource. The surgeon(s) who will perform the operation have already been assigned to the patients and is a dedicated resource. The assignment of the anesthesia team has been considered as a decision problem and a flexible resource. In this study, cooperative operations are also considered. A mixed integer linear programming (MILP) model is proposed for the problem. Since the pr

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28

Yang, Wenda, Minggong Wu, Xiangxi Wen, and Kexin Bi. "A Joint Optimization Method for Cooperative Detection Resources Based on Channel Capacity." International Journal of Aerospace Engineering 2022 (August 18, 2022): 1–16. http://dx.doi.org/10.1155/2022/8418426.

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Aiming at the resource optimization problem in the cooperative detection task, the objective function is constructed based on the channel capacity, and the artificial bee colony (ABC) algorithm is improved to realize the joint optimization of the UAV swarm trajectory and radiation power. Firstly, a multiple input multiple output (MIMO) cooperative detection model is constructed. Then, based on the perspective of information theory, the channel capacity of the cooperative detection model is derived and used as the objective function for optimizing the detection resources of UAV swarm. Then, the

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29

Liu, Wei, Changyun Liu, Xiangke Guo, Sheng He, and Liangyou Fan. "Solving the Multisensor Resource Scheduling Problem for Missile Early Warning by a Hybrid Discrete Artificial Bee Colony Algorithm." Journal of Sensors 2022 (October 25, 2022): 1–16. http://dx.doi.org/10.1155/2022/5094415.

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Aiming at the problem of multisensor resource scheduling in missile early warning operation, a scheduling decomposition strategy for missile early warning tasks under cooperative detection is proposed. Taking the detection benefit factor, target threat factor, and handover factor as the fitness function, we establish a sensor-subtask assignment (SSA) model and propose a hybrid discrete artificial bee colony (HDABC) algorithm to solve the optimal solution of the SSA model. The HDABC algorithm has the following improvements: in the initialization stage, a sensor-subtask-based coding method is de

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30

Zemali, Elamine, and Abdelmadjid Boukra. "CS-ABC: a cooperative system based on artificial bee colony to resolve the DNA fragment assembly problem." International Journal of Data Mining and Bioinformatics 21, no. 2 (2018): 145. http://dx.doi.org/10.1504/ijdmb.2018.096407.

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31

Zemali, Elamine, and Abdelmadjid Boukra. "CS-ABC: a cooperative system based on artificial bee colony to resolve the DNA fragment assembly problem." International Journal of Data Mining and Bioinformatics 21, no. 2 (2018): 145. http://dx.doi.org/10.1504/ijdmb.2018.10017727.

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32

Ma, Kai, Xuemei Liu, Guoqiang Li, Shubing Hu, Jie Yang, and Xinping Guan. "Resource allocation for smart grid communication based on a multi-swarm artificial bee colony algorithm with cooperative learning." Engineering Applications of Artificial Intelligence 81 (May 2019): 29–36. http://dx.doi.org/10.1016/j.engappai.2018.12.002.

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33

Bulygina, Olga V., Nikolay S. Kulyasov, and Denis D. Yartsev. "Directions for modifying the artificial bee colony algorithm to optimize control parameters for complex systems." Journal Of Applied Informatics 19, no. 1 (2024): 28–37. http://dx.doi.org/10.37791/2687-0649-2024-19-1-28-37.

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In recent years, bioinspired algorithms based on the use of a population approach and a probabilistic search strategy have become especially popular among researchers involved in multidimensional and multicriteria optimization. Such algorithms are based on the principles of cooperative behavior of a decentralized self-organizing colony of living organisms (bees, ants, birds, etc.) to achieve certain goals (for example, to meet nutritional needs). However, their practical application encounters a number of difficulties leading to a decrease in convergence. This article discusses the possibility

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34

Yan, Xiaohong, and Renwen Chen. "Application Strategy of Unmanned Aerial Vehicle Swarms in Forest Fire Detection Based on the Fusion of Particle Swarm Optimization and Artificial Bee Colony Algorithm." Applied Sciences 14, no. 11 (2024): 4937. http://dx.doi.org/10.3390/app14114937.

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Unmanned aerial vehicle (UAV) swarm intelligence technology has shown unique advantages in agricultural and forestry disaster detection, early warning, and prevention with its efficient and precise cooperative operation capability. In this paper, a systematic application strategy of UAV swarms in forest fire detection is proposed, including fire point detection, fire assessment, and control measures, based on the fusion of particle swarm optimization (PSO) and the artificial bee colony (ABC) algorithm. The UAV swarm application strategy provides optimized paths to quickly locate multiple mount

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35

Kumar, Ashok S., and T. Sudha. "Combined Optimization of Clustering Using Artificial Bee Colony Algorithm and Cooperative Beamforming in Green Cognitive Radio Networks with Relays." Procedia Technology 24 (2016): 888–95. http://dx.doi.org/10.1016/j.protcy.2016.05.157.

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36

Yang, Lina, Xu Sun, and Zhenlong Li. "An Efficient Framework for Remote Sensing Parallel Processing: Integrating the Artificial Bee Colony Algorithm and Multiagent Technology." Remote Sensing 11, no. 2 (2019): 152. http://dx.doi.org/10.3390/rs11020152.

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Remote sensing (RS) image processing can be converted to an optimization problem, which can then be solved by swarm intelligence algorithms, such as the artificial bee colony (ABC) algorithm, to improve the accuracy of the results. However, such optimization algorithms often result in a heavy computational burden. To realize the intrinsic parallel computing ability of ABC to address the computational challenges of RS optimization, an improved multiagent (MA)-based ABC framework with a reduced communication cost among agents is proposed by utilizing MA technology. Two types of agents, massive b

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37

Guo, Jun, Yang Li, Baigang Du, Xiang Sun, and Kaipu Wang. "A multi-population cooperative coevolution artificial bee colony algorithm for partial multi-robotic disassembly line balancing problem considering preventive maintenance scenarios." Advanced Engineering Informatics 62 (October 2024): 102750. http://dx.doi.org/10.1016/j.aei.2024.102750.

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38

Hong, Wei, Wenjing Yin, and Shuling Xu. "Collaborative truck-robot routing problem with meal delivery for the elderly on the personalized needs." International Journal of Industrial Engineering Computations 15, no. 3 (2024): 615–26. http://dx.doi.org/10.5267/j.ijiec.2024.5.005.

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With the development of a new generation of information technology, smart elderly care plays an important role in promoting the construction of elderly care services. The emerging application tools provide door-to-door meal service for urban elderly groups, solving meal problems for special and ordinary elderly with different priority levels and penalty costs of violating time windows. Based on this, considering the personalized needs of the elderly group, this study examines the route optimization problem of cooperative delivery of elderly meals by trucks and robots, and builds a mixed intege

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39

Xing, Huaixi, Qinghua Xing, and Kun Wang. "A Joint Allocation Method of Multi-Jammer Cooperative Jamming Resources Based on Suppression Effectiveness." Mathematics 11, no. 4 (2023): 826. http://dx.doi.org/10.3390/math11040826.

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This paper studies the resource allocation problem when multiple jammers follow the aircraft formation to support ground penetration. A joint optimization allocation method of multi-jammer beam-power based on the improved artificial bee colony (IABC) algorithm is proposed. The air-to-ground “many-to-many” assault of the multi-jammer cooperative suppression jamming model is given. The constant false alarm probability detection model of the networked radar is used to evaluate the suppression effect, and a coordinated control model of multi-jammer jamming beams and emitting power is established.

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Wu, Wan-Zhong, Hong-Yan Sang, Quan Ke Pan, Qiu-Yang Han, and Heng-Wei Guo. "A cooperative discrete artificial bee colony algorithm with Q-learning for solving the distributed permutation flowshop group scheduling problem with preventive maintenance." Swarm and Evolutionary Computation 95 (June 2025): 101910. https://doi.org/10.1016/j.swevo.2025.101910.

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Chitradurga Manjunath, Manasa, and Blessed Prince Pallayan. "Artificial Bee Colony Algorithm-based Feature Selection and Hybrid ML Framework for Efficient Rice Yield Prediction." International journal of electrical and computer engineering systems 15, no. 3 (2024): 235–46. http://dx.doi.org/10.32985/ijeces.15.3.3.

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India's economy predominantly depends on monsoon and agricultural output. Agribusiness products contribute to nearly a quarter of its gross domestic product and 58% of its population depends on agriculture for their livelihood. Certain crops, like rice, are vital to its food security being the most widely grown crop and accounting for one-third production of foodgrains in India. Understanding and enhancing its production is critical in ensuring food availability and promoting sustainable agricultural practices. Rice yield prediction has been a most researched area in the agriculture domain. Ma

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MAKAS, Hasan, and Nejat YUMUŞAK. "Balancing exploration and exploitation by using sequential execution cooperation between artificial bee colony and migrating birds optimization algorithms." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 24 (2016): 4935–56. http://dx.doi.org/10.3906/elk-1404-45.

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43

Wu, Daqing, and Jianguo Zheng. "A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization." Discrete Dynamics in Nature and Society 2012 (2012): 1–22. http://dx.doi.org/10.1155/2012/578064.

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A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO) and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC) for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback o

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44

Li, Rong, Qing Liu, and Lei Wang. "A Collaborative Optimization Model for Metro Passenger Flow Control Considering Train–Passenger Symmetry." Symmetry 17, no. 6 (2025): 937. https://doi.org/10.3390/sym17060937.

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Due to the unbalanced temporal and spatial distribution of the passenger flow on metro lines during peak hours, the implementation of passenger flow control strategies effectively ensures operational safety and travel efficiency for passengers. In this study, we analyze the coupling relationship between trains and passengers, introduce train-stopping state variables, and synergistically optimize both train operation schedules and station passenger flow control. Aiming to minimize the total passenger delay time and maximize the number of boarding passengers, we consider four constraints: the tr

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45

Meng, Qicheng, Qingjun Qu, Kai Chen, and Taihe Yi. "Multi-UAV Path Planning Based on Cooperative Co-Evolutionary Algorithms with Adaptive Decision Variable Selection." Drones 8, no. 9 (2024): 435. http://dx.doi.org/10.3390/drones8090435.

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When dealing with UAV path planning problems, evolutionary algorithms demonstrate strong flexibility and global search capabilities. However, as the number of UAVs increases, the scale of the path planning problem grows exponentially, leading to a significant rise in computational complexity. The Cooperative Co-Evolutionary Algorithm (CCEA) effectively addresses this issue through its divide-and-conquer strategy. Nonetheless, the CCEA needs to find a balance between computational efficiency and algorithmic performance while also resolving convergence difficulties arising from the increased num

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46

Arif, Ullah, Ahmad Khan Shakeel, Alam Tanweer, Luma-Osmani Shkurte, and Sadie Mahanz. "Heart disease classification using various heuristic algorithms." International Journal of Advances in Applied Sciences (IJAAS) 11, no. 2 (2022): 158–67. https://doi.org/10.11591/ijaas.v11.i2.pp158-167.

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In the health sector, the computer-aided diagnosis (CAD) system is a rapidly growing technology because medical diagnostic systems make a huge change as compared to the traditional system. Now a day huge availability of medical data and it needs a proper system to extract them into useful knowledge. Heart disease accounts to be the leading cause of death worldwide. Heuristic algorithms have been exposed to be operative in supporting making decisions and classification from the large quantity of data produced by the healthcare sector. Classification is a prevailing heuristic approach which is c

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Ullah, Arif, Shakeel Ahmad Khan, Tanweer Alam, Shkurte Luma-Osmani, and Mahanz Sadie. "Heart disease classification using various heuristic algorithms." International Journal of Advances in Applied Sciences 11, no. 2 (2022): 158. http://dx.doi.org/10.11591/ijaas.v11.i2.pp158-167.

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<span>In the health sector, the computer-aided diagnosis (CAD) system is a rapidly growing technology because medical diagnostic systems make a huge change as compared to the traditional system. Now a day huge availability of medical data and it needs a proper system to extract them into useful knowledge. Heart disease accounts to be the leading cause of death worldwide. Heuristic algorithms have been exposed to be operative in supporting making decisions and classification from the large quantity of data produced by the healthcare sector. Classification is a prevailing heuristic approac

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Liu, Zhiguo, Zhengxia Liu, Lin Wang, and Xiaoyong Jin. "The satellite network cache placement strategy based on content popularity and node collaboration." PLOS ONE 19, no. 8 (2024): e0307280. http://dx.doi.org/10.1371/journal.pone.0307280.

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Proposed is a Satellite network cache placement strategy (PNCCP) based on popularity and node cooperation to address the issue of significant delays in end-to-end connectivity due to instability among satellites. Initially, the strategy employs spectral clustering algorithm to partition the satellite network’s topology, limiting the retrieval scope of content and reducing unnecessary propagation delays. Within each partition, a cache collaboration open mechanism among satellites is devised to share cache resources, utilizing the proximity of neighboring nodes to share popular content and cache

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COSAR, Mustafa. "Path Planning via Swarm Intelligence Algorithms in Unmanned Aerial Vehicle Population." Eurasia Proceedings of Science Technology Engineering and Mathematics 26 (December 30, 2023): 439–50. http://dx.doi.org/10.55549/epstem.1411059.

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Unmanned Aerial Vehicle (UAV) is an autonomous aerial vehicle capable of operating autonomously or in swarm cooperation, performing various tasks in civilian and military domains that exceed human capabilities. These vehicles, which can be produced in different models with varying hardware and software features, include flight control systems, route tracking systems, sensors, and numerous additional components. UAVs have the ability to process data from themselves, the control center, and the external environment. Data processing enables functions such as flight management, swarm optimization,

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Brajević, Ivona, Predrag S. Stanimirović, Shuai Li, Xinwei Cao, Ameer Tamoor Khan, and Lev A. Kazakovtsev. "Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems." Mathematics 10, no. 23 (2022): 4555. http://dx.doi.org/10.3390/math10234555.

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Engineering design optimization problems are difficult to solve because the objective function is often complex, with a mix of continuous and discrete design variables and various design constraints. Our research presents a novel hybrid algorithm that integrates the benefits of the sine cosine algorithm (SCA) and artificial bee colony (ABC) to address engineering design optimization problems. The SCA is a recently developed metaheuristic algorithm with many advantages, such as good search ability and reasonable execution time, but it may suffer from premature convergence. The enhanced SCA sear

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