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22nd International Conference on Information and Software Technologies (ICIST)

作者： Wozniak, Marcin Silesian Tech Univ Inst Math Kaszubska 23 PL-44100 Gliwice Poland

ISBN: (纸本)9783319462547;9783319462530

In the article an approach toward novel method for image features correction is proposed. For the input image developed swarm intelligence technique is applied to improve brightness, contrast, sharpen presentation and improve gamma correction. The following sections present proposed model of the correction techniques with applied swarm intelligence approach. Experimental results on a set of test images are presented with a discussion of achieved improvements.

关键词： Image processing swarm algorithm

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AGV controller based on improved particle swarm optimization

AGV controller based on improved particle swarm optimization

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Chinese Automation Congress (CAC)

作者： Zhou, Xinmin Zhang, Yimei Chen, Tianwei Wuhan Univ Technol Sch Automat Wuhan Hubei Peoples R China

ISBN: (纸本)9781728113128

As a high-end equipment for automated logistics, AGV has been widely used in workshops, warehouses and other places. AGV achieves the purpose of navigation through the identification and tracking of the route signs, which requires improving the tracking accuracy. A particle swarm algorithm is used to optimize the AGV controller parameters so that the AGV tracking bias is as small as possible. Firstly, the kinematics model of AGV is analyzed, and the PID controller optimized by particle swarm optimization algorithm is established. Then the car control is simulated in Matlab. By comparing the performance indexes of the system before and after optimization, the optimized PID controller can be seen. The performance of the car controller is significantly improved.

关键词： AGV swarm algorithm PID controller

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Optimizing Heart Disease Prediction Using a Hybrid Dynamic swarm Evolution Approach

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SN Computer Science 2024年第8期5卷 1104页

作者： Parikh, Vanssh Sharma, Bhoomi Byotra, Arnav Malhotra, Amarjit Department of Information Technology Netaji Subhas University of Technology New Delhi India

Early identification of Cardiovascular issues is crucial due to the high mortality rate associated with heart disease. This research assesses machine learning algorithms for heart disease prediction using tabular datasets. Previous researchers suggested Tree-based models like Random Forest, XGBoost, and Decision Trees and various hyper-parameter optimization methods including Grid Search, Random Search, swarm and Evolutionary algorithms which excel in accuracy and robustness. However, they are computationally inefficient and less effective in dynamic settings. A novel Hybrid swarm Evolution Optimization (HySEOpt) is introduced, which adjusts mutation rates based on performance curves and utilizes parallel processing for faster optimization achieving 98.01% accuracy. HySEOpt enhances model’s quality and robustness, addressing limitations of existing methods and contributes to hyper-parameter optimization in predictive healthcare modeling. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Evolutionary algorithm Heart disease prediction Hybrid swarm evolution Hyper-parameter optimisation swarm algorithm

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swarm Intelligent Routing Solution for Wireless Sensor Networks

Swarm Intelligent Routing Solution for Wireless Sensor Netwo...

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35th Annual IEEE Conference on Local Computer Network (LCN)

作者： Gajurel, Sanjaya Heiferling, Mark Bluetronix Inc Cleveland OH 44105 USA

ISBN: (纸本)9781424483891

In this paper, we have developed a swarm Routing Protocol that uses swarm Autonomous Routing algorithm (SARA) for Wireless Sensor Networks (WSN) and developed WSN prototypes (accelerometer and temperature) by embedding SARA into COTS devices. SARA provides intelligent routing solution for MANET where the rebroadcasting node (COTS device) is chosen from among the neighbor nodes by comparing the pheromones associated with each neighbor. Nodes learn by modifying the pheromone values associated with neighbor nodes when a packet is received. Unlike other Ad Hoc routing protocols, there are no control messages used to determine the route to destinations;data packets reach to destination hopping via designated rebroadcasting nodes. This will significantly reduces the network overhead and provide very efficient bandwidth solution. Moreover, the protocol does not use routing tables other than local pheromone tables and hence provide very efficient routing solution. The QualNet Simulation results show that the network performance using SARA has outperformed other currently used MANET routing protocols. The successful accelerometer and temperature sensor demonstrations were performed using Freescale's 13192SARD boards.

关键词： MANET Wireless Senosr Network (WSN) Prototypes swarm Intelligence Location Service Routing Protocol Reconfigurability swarm algorithm

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Development of swarm Intelligence Leader-Vicsek-Model for Multi-AGV Path Planning 20

Development of Swarm Intelligence Leader-Vicsek-Model for Mu...

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20th International Symposium on Communications and Information Technologies (ISCIT)

作者： Lin, Shiwei Liu, Ang Kong, Xiaoying Wang, Jack Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW Australia

ISBN: (纸本)9781665449588

Automatic guided vehicle (AGV) is the mobile robot widely used in the industry, and multiple AGVs are usually involved for industrial intelligence. Path planning for multi-AGV is essential for automatic transportation, and we propose a swarm-based approach that is inspired by biological subjects. The dynamic virtual leader is assigned for updating the locations and angles for the Leader-Vicsek-Model, and multiple agents are assigned through the centralized approach while the path planning is achieved through the decentralized method.

关键词： multi-AGV path planning swarm algorithm

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Optimal PID Controller Autotuning Design for MIMO Nonlinear Systems Based on the Adaptive SLP algorithm

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2021年第1期19卷 392-403页

作者： Pongfai, Jirapun Angeli, Chrissanthi Shi, Peng Su, Xiaojie Assawinchaichote, Wudhichai King Mongkuts Univ Technol Fac Engn Dept Elect & Telecommun Engn Bangkok Thailand Univ West Attica Fac Engn Dept Elect & Elect Engn Athens Greece Univ Adelaide Adelaide SA Australia Victoria Univ Melbourne Vic Australia Chongqing Univ Coll Automat Chongqing Peoples R China

In this paper, an adaptive swarm learning process (SLP) algorithm for designing the optimal proportional integral and derivative (PID) parameter for a multiple-input multiple-output (MIMO) control system is proposed. The SLP algorithm is proposed to improve the performance and convergence of PID parameter autotuning by applying the swarm algorithm and the learning process. The adaptive SLP algorithm improves the stability, performance and robustness of the traditional SLP algorithm to apply it to a MIMO control system. It can update the online weights of the SLP algorithm caused by the errors in the settling time, rise time and overshoot of the system based on a stable learning rate. The gradient descent is applied to update the weights. The stable learning rate is verified based on the Lyapunov stability theorem. Additionally, simulations are performed to verify the superiority of the algorithm in terms of performance and robustness. Results that compare the adaptive SLP algorithm with the traditional SLP, a neural network (NN), the genetic algorithm (GA), the particle swarm and optimization (PSO) algorithm and the kidney-inspired algorithm (KIA) based on a two-wheel inverted pendulum system are presented. With respect to performance and robustness, the adaptive SLP algorithm provides a better response than the traditional SLP, NN, GA, PSO and KIA.

关键词： Autotuning inverted pendulum learning algorithm multiple-input multiple-output (MIMO) optimal control PID controller swarm algorithm

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Service Composition Instantiation Based on Cross-Modified Artificial Bee Colony algorithm

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China Communications 2016年第10期13卷 233-244页

作者： Lei Huo Zhiliang Wang School of Automation University of Science and Technology Beijing School of Computer and Science University of Science and Technology Beijing

Internet of things(IoT) imposes new challenges on service composition as it is difficult to manage a quick instantiation of a complex services from a growing number of dynamic candidate services. A cross-modified Artificial Bee Colony algorithm(CMABC) is proposed to achieve the optimal solution services in an acceptable time and high accuracy. Firstly, web service instantiation model was established. What is more, to overcome the problem of discrete and chaotic solution space, the global optimal solution was used to accelerate convergence rate by imitating the cross operation of Genetic algorithm(GA). The simulation experiment result shows that CMABC exhibited faster convergence speed and better convergence accuracy than some other intelligent optimization algorithms.

关键词： optimization of service composition optimal service instantiation artificial bee colony algorithm swarm algorithm cross strategy

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Equilibrium optimizer: a comprehensive survey

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第10期83卷 29617-29666页

作者： Al-Betar, Mohammed Azmi Abu Doush, Iyad Makhadmeh, Sharif Naser Al-Naymat, Ghazi Alomari, Osama Ahmad Awadallah, Mohammed A. Ajman Univ Coll Engn & Informat Technol Dept Informat Technol POB 346 Ajman U Arab Emirates Ajman Univ Artificial Intelligence Res Ctr AIRC POB 346 Ajman U Arab Emirates Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol POB 50 Irbid Jordan Yarmouk Univ Comp Sci Dept Irbid Jordan Amer Univ Kuwait Coll Engn & Appl Sci Comp Dept Salmiya Kuwait Univ Petra Dept Data Sci & Artificial Intelligence POB 11196 Amman Jordan Abu Dhabi Univ Coll Engn Dept Comp Sci & Informat Technol Abu Dhabi U Arab Emirates Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine

Equilibrium optimizer (EO) is a recent optimization method inspired by the physical equation of the mass balance that provides the conservation of mass entering, leaving, and generating in a control volume and the system always reaches an equilibrium point. It is a fast-growing algorithm that has been adopted by several researchers due to its successful features, such as simplicity, parameter-less, derivative-free, and sound-and-complete. In this survey paper, a comprehensive analysis of EO and its evolution, since its introduction in 2020 until now, is provided. Its performance and convergence behavior are initially studied and compared against eight well-established methods using 23 mathematical optimization functions which reveals its superiority. Since EO is established to deal with a single objective and free optimization problems of the continuous domain, a comprehensive analysis of the different EO versions which are either modified versions or hybridized versions are introduced to deal with different search spaces of optimization problems. Furthermore, this research aims to provide a deep analysis of the applications of the EO algorithm, including the population type, the optimizer version used, and the main contribution. This is realized by its successful applications in different domains such as scheduling and planning, power and electrical engineering, civil and environmental engineering, communication and networking, image processing, etc. A discussion about the different EO versions and applications is provided to highlight the pros and cons of such algorithms. This discussion leads this survey to end up providing several possible research directions that can be followed in the future. Overall, this survey provides a comprehensive analysis of Equilibrium Optimizer performance and applications and contributes to the broader field of optimization algorithms.

关键词： swarm algorithm Equilibrium optimizer Metaheuristics Optimization

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Bat4CEP: a bat algorithm for mining of complex event processing rules

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APPLIED INTELLIGENCE 2022年第13期52卷 15143-15163页

作者： Bruns, Ralf Dunkel, Juergen Hannover Univ Appl Sci & Arts Dept Comp Sci Ricklinger Stadtweg 120 D-30459 Hannover Germany

Complex Event Processing (CEP) is a modern software technology for the dynamic analysis of continuous data streams. CEP is able of searching extremely large data streams in real time for the presence of event patterns. So far, specifying event patterns of CEP rules is still a manual task based on the expertise of domain experts. This paper presents a novel bat-inspired swarm algorithm for automatically mining CEP rule patterns that express the relevant causal and temporal relations hidden in data streams. The basic suitability and performance of the approach is proven by extensive evaluation with both synthetically generated data and real data from the traffic domain.

关键词： Bat algorithm swarm algorithm Rule learning Complex event processing

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Revisiting wavefront construction with collective agents: an approach to foraging

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swarm INTELLIGENCE 2014年第2期8卷 113-138页

作者： Simonin, Olivier Charpillet, Francois Thierry, Eric INSA Lyon CITI Inria Lab Villeurbanne France Maia Team LORIA Lab Inria Nancy Grand Est Nancy France ENS Lyon Lyon France

We consider the problem of coordinating a team of agents that have to collect disseminated resources in an unknown environment. We are interested in approaches in which agents collectively explore the environment and build paths between home and resources. The originality of our approach is to simultaneously build an artificial potential field (APF) around the agents' home while foraging. We propose a multi-agent model defining a distributed and asynchronous version of Barraquand et al. Wavefront algorithm. Agents need only to mark and read integers locally on a grid, that is, their environment. We prove that the construction converges to the optimal APF. This allows the definition of a complete parameter-free foraging algorithm, called c-marking agents. The algorithm is evaluated by simulation, while varying the foraging settings. Then we compare our approach to a pheromone-based algorithm. Finally, we discuss requirements for implementation in robotics.

关键词： swarm algorithm Foraging task Artificial potential field Ant algorithms

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