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Odor source localization of multi-robots with swarm intelligence algorithms: A review

FRONTIERS IN NEUROROBOTICS 2022年 16卷 949888页

作者： Wang, Junhan Lin, Yuezhang Liu, Ruirui Fu, Jun Zhejiang Gongshang Univ Sch Comp Sci & Informat Engn Artificial Intelligence Things & Robot Lab Hangzhou Peoples R China

The use of robot swarms for odor source localization (OSL) can better adapt to the reality of unstable turbulence and find chemical contamination or hazard sources faster. Inspired by the collective behavior in nature, swarm intelligence (SI) is recognized as an appropriate algorithm framework for multi-robot system due to its parallelism, scalability and robustness. Applications of SI-based multi-robots for OSL problems have attracted great interest over the last two decades. In this review, we firstly summarize the trending issues in general robot OSL field through comparing some basic counterpart concepts, and then provide a detailed survey of various representative SI algorithms in multi-robot system for odor source localization. The research field originates from the first introduction of the standard particle swarm optimization (PSO) and flourishes in applying ever-increasing quantity of its variants as modified PSOs and hybrid PSOs. Moreover, other nature-inspired SI algorithms have also demonstrated the diversity and exploration of this field. The computer simulations and real-world applications reported in the literatures show that those algorithms could well solve the main problems of odor source localization but still retain the potential for further development. Lastly, we provide an outlook on possible future research directions.

关键词： odor source localization swarm intelligence algorithm multi-robot system particle swarm optimization mobile robot nature-inspired computation

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A Survey of Using swarm intelligence algorithms in IoT

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SENSORS 2020年第5期20卷 1420-1420页

作者： Sun, Weifeng Tang, Min Zhang, Lijun Huo, Zhiqiang Shu, Lei Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Peoples R China Nanjing Agr Univ Coll Engn Nanjing 210031 Peoples R China Univ Lincoln Lincoln England

With the continuing advancements in technologies (such as machine to machine, wireless telecommunications, artificial intelligence, and big data analysis), the Internet of Things (IoT) aims to connect everything for information sharing and intelligent decision-making. swarm intelligence (SI) provides the possibility of SI behavior through collaboration in individuals that have limited or no intelligence. Its potential parallelism and distribution characteristics can be used to realize global optimization and solve nonlinear complex problems. This paper reviews representative SI algorithms and summarizes their applications in the IoT. The main focus consists in the analysis of SI-enabled applications to wireless sensor network (WSN) and discussion of related research problems in the WSN. Also, we concluded SI-based applications in other IoT fields, such as SI in UAV-aided wireless network. Finally, possible research prospects and future trends are drawn.

关键词： swarm intelligence algorithm ant colony optimization particle swarm optimization artificial bee colony algorithm wireless sensor network UAV

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Iris recognition method based on improved sparrow search algorithm and BPNN

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2025年第1期7卷

作者： Su, Ge Tian, Ye Changchun Univ Sci & Technol Sch Comp Sci & Technol Changchun 130022 Peoples R China Changchun Univ Sci & Technol Zhongshan Inst Zhongshan 528437 Peoples R China

Iris recognition is a very popular and efficient biometric method that has a wide range of applications in finance, security and many other areas. The uniqueness and invariance of the iris makes it highly accurate in identification. Although there are many approaches to iris recognition, such as deep learning based methods, drawbacks such as high computational complexity, high memory requirements, and long training times still exist. Since BP neural networks have the advantages of simple structure and short training time, this paper, an iris recognition method based on improved sparrow search algorithm (CRSSA) optimized back propagation neural network (BPNN) is proposed. The sparrow search algorithm is improved using improved Kent chaos mapping strategy, Dynamic adaptive weight strategy and Logarithmic spiral-based random walk strategy. Then the improved algorithm is used to optimize the BP neural network. Finally, the improved network is used as a classifier for iris recognition. Preprocessing of iris images using Hough transform based processing. The iris image is processed using feature extraction methods based on first order statistical measures and second order statistical measures (F-S) and finally iris recognition is performed using the method proposed in this paper. The experimental results show that the method proposed in this paper exhibits higher recognition accuracy and stability than other algorithms in the experiment on CSAIA-iris-V4 and JLU-4.0 datasets. The improved sparrow search algorithm (CRSSA) proposed in this paper also performs better than the other algorithms in the experiments in terms of convergence accuracy and stability.

关键词： sparrow search algorithm iris recognition random walk swarm intelligence algorithm

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On the comparative performance of recent swarm intelligence based algorithms for optimization of real-life Sterling cycle operated refrigeration/liquefaction system

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ARTIFICIAL intelligence REVIEW 2023年第2期56卷 1297-1317页

作者： Raja, Bansi D. Patel, Vivek K. Savsani, Vimal J. Yildiz, Ali Riza Indus Univ Dept Mech Engn Ahmadabad Gujarat India Pandit Deendayal Energy Univ Sch Technol Dept Mech Engn Gandhinagar Gujarat India Canadore Coll Dept Engn North Bay ON Canada Uludag Univ Automot Engn Dept Bursa Turkey

In the recent past year of 2020-2021, researchers proposed many swarm intelligence based algorithms. In the present work, an effort has been made to compare the performance of these algorithms for the real-life constraint optimization problem. swarm intelligence-based algorithms developed during 2020-2021 such as GEO, WHO, MPA, JSO, ChoA, MA, BWO, AO, COOT, and TSA are considered in the present work. These algorithms are implemented for the performance optimization of the Sterling cycle operated refrigeration/liquefaction system. Four operating variables and two output constraints of the Sterling cycle based system are considered for optimization. Comparative results are presented with statistical data to judge the performance of the algorithm and subsequently identify the statistical significance and rank of the algorithm. The effect of various constraint handling methods on the performance of algorithms is evaluated and presented. The behaviour of constraint handling methods is analyzed and presented with statistical data. Statistical analysis is also performed to observe whether the constraint handling methods produce a significant difference on the output of the considered algorithm. The effect of output constraints on the performance of algorithms is also evaluated and presented. Finally, the convergence behaviour of the competitive algorithms is obtained and demonstrated.

关键词： swarm intelligence algorithm Constraint optimization Comparative performance Constraint handling techniques Convergence behaviour

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Enhanced UAV Pursuit-Evasion Using Boids Modelling:A Synergistic Integration of Bird swarm intelligence and DRL

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Computers, Materials & Continua 2024年第9期80卷 3523-3553页

作者： Weiqiang Jin Xingwu Tian Bohang Shi Biao Zhao Haibin Duan Hao Wu School of Information and Communications Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China China Academy of Electronics and Information Technology China Electronics Technology Group Corporation(CETC)Beijing100041China Department ofAutomatic Control School of Automation Science and Electrical EngineeringBeihangUniversityBeijing100191China

TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and *** address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our *** integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion *** further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic *** also leverage self-play training to continuously optimize the performance of pursuit *** experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each *** simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed *** overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.

关键词： UAV pursuit-evasion swarm intelligence algorithm Boids model deep reinforcement learning self-play training

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swarm intelligence-Based Feature Selection: An Improved Binary Grey Wolf Optimization Method 14th

Swarm Intelligence-Based Feature Selection: An Improved Bina...

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14th International Conference on Knowledge Science, Engineering, and Management (KSEM)

作者： Li, Wenqu Kang, Hui Feng, Tie Li, Jiahui Yue, Zhiru Sun, Geng Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)9783030821531;9783030821524

Feature selection can effectively reduce the number of features and improve the accuracy of classification, so that reducing the computational burden and improving the performance of machine learning. In this paper, we propose an improved binary grey wolf optimization (IBGWO) algorithm for a wrapper-based feature selection method. Aiming at the shortcomings of grey wolf optimization (GWO) for feature selection, we first propose a enhanced opposition-based learning (E-OBL) initialization method to enhance the performance of initial solutions. Second, a local search strategy is introduced to balance the exploitation and exploration abilities of the IBGWO. Finally, a novel update mechanism is proposed for improving the population diversity and exploration capability of the algorithm. Simulations are conducted by using 16 well-known datasets, and the results show that the proposed method outperforms other benchmark algorithms on 12 datasets, and the introduced improved factors are suitable and effective.

关键词： Feature selection Classification swarm intelligence algorithm Grey wolf optimization

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Reconstituted data-driven air conditioning energy consumption prediction system employing occupant-orientated probability model as input and swarm intelligence optimization algorithms

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ENERGY 2024年 288卷

作者： Zhang, Chengyu Ma, Liangdong Xing, Han Zhao, Tianyi Dalian Univ Technol Inst Bldg Energy Dalian Peoples R China PCI Technol Grp Guangzhou Peoples R China

With recent energy and environmental crises, the energy consumption prediction of air conditioning (AC) is essential. Notably, existing general prediction systems use the air temperature, real-time AC usage, time, oc-cupancy, and AC historical energy consumption as inputs and employ feedforward neural networks. However, such systems face certain challenges. First, the input system may be difficult to reflect the real-time interaction among the building environment, occupants, and energy system;Second, the algorithms need to be upgraded to further improve accuracy, speed up convergence, and avoid overfitting. his paper introduces a novel parameter (group occupant behavior probability, GOBP) in the input system;and uses swarm intelligence algorithms with chaotic mapping and adaptive weight adjustment as optimization with LSTM and BP as the basic algorithms. The verification results obtained from five different buildings indicate the following: (1) The novel prediction system improved prediction performance with 20.08%-78.37 % of MAPE and 3.99%-53.28 % of CV-RMSE, however, it will increase 148.70s-1065.11s of calculation time. (2) For accuracy, using GOBP will improve the 0.30%-51.87 % and 0.18%-64.77 % by using swarm intelligence optimization algorithms. (3) This paper discusses the priority of selecting suitable inputs, algorithms, and optimization to combine a whole prediction system.

关键词： AC energy consumption Energy consumption prediction Group occupant behavior probability swarm intelligence algorithm Long short-term memory neural network

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WOAD3QN-RP: An intelligent routing protocol in wireless sensor networks - A swarm intelligence and deep reinforcement learning based approach

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 246卷

作者： Yang, Xuan Yan, Jiaqi Wang, Desheng Xu, Yonggang Hua, Gang China Univ Min & Technol Sch Informat & Control Engn Xuzhou Peoples R China

Wireless Sensor Networks (WSN) are a crucial part of the Internet of Things (IoT), and research on WSN routing protocols has always been a hot topic in academia. However, traditional WSN routing protocols have limited utilization of available information during the routing decision process, leading to challenges such as insufficient adaptability to network topology changes, high communication delays, and short network lifetimes. To address these issues, this paper proposes an innovative intelligent routing algorithm WOAD3QN-RP, which cleverly integrates swarm intelligence algorithms and deep reinforcement learning. The WOAD3QN-RP not only effectively reduces delay but also balances energy consumption and flexibly adapts to changes in network topology, while simultaneously determining the optimal multi -hop path, effectively extending the lifetime of the network. Firstly, the WOAD3QN-RP algorithm employs the Whale Optimization algorithm (WOA) to determine the optimal cluster heads (CHs). In the process of selecting CHs, the algorithm comprehensively considers key factors such as the residual energy of nodes, node distance, and communication delay, thereby significantly improving the accuracy and efficiency of CH selection, which contributes to better energy distribution and performance of the network. Secondly, in terms of multi -hop path selection, WOAD3QN-RP uses a dueling double deep Q -network (D3QN) to determine the optimal multi -hop path. Through utilizing neural networks to interact with the environment, intelligent agents are trained to learn routing policies to adapt to dynamic changes in the network topology and ensure the balance between energy consumption and multi -hop routing performance. Experimental results show that WOAD3QN-RP exhibits significant advantages over existing routing protocols in terms of network lifetime, energy efficiency, and communication delay.

关键词： Wireless sensor networks Clustering Routing swarm intelligence algorithm Deep reinforcement learning

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swarm intelligence in Internet of Medical Things: A Review

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SENSORS 2023年第3期23卷 1466-1466页

作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Izadi, Navid Hoseini Gravina, Raffaele Kabir, H. M. Dipu Nahavandi, Darius Alinejad-Rokny, Hamid Khosravi, Abbas Acharya, U. Rajendra Nahavandi, Saeid Fortino, Giancarlo Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3216 Australia Fasa Univ Fac Engn Dept Comp Engn Vali asr Blvd Fasa *** Iran Isfahan Univ Technol Dept Elect & Comp Engn Daneshgah e Sanati Hwy Esfahan 83111 Iran Univ Calabria Dept Informat Modeling Elect & Syst DIMES I-87036 Cosenza Italy UNSW Grad Sch Biomed Engn Biomed Machine Learning Lab BML Sydney NSW 2052 Australia Univ New South Wales UNSW Sydney UNSW Data Sci Hub Sydney NSW 2052 Australia Macquarie Univ AI Enabled Proc AIP Res Ctr Hlth Data Analyt Program Sydney NSW 2109 Australia Ngee Ann Polytech Dept Elect & Comp Engn Singapore 599489 Singapore SUSS Univ Sch Sci & Technol Dept Biomed Engn Singapore 599494 Singapore Asia Univ Dept Biomed Informat & Med Engn Taichung 41354 Taiwan Harvard Univ Harvard Paulson Sch Engn & Appl Sci Allston MA 02134 USA

Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

关键词： internet of things internet of medical things internet of things in health swarm intelligence algorithm wearable devices wireless sensor network

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A Server Placement algorithm for Reducing Risk and Improving Power Utilization in Data Centers

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Tsinghua Science and Technology 2024年第1期29卷 158-173页

作者： Rui Chen Huikang Huang Xiaoxuan Luo Weiwei Lin School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Peng Cheng Laboratory Shenzhen 518066China

As the power demand in data centers is increasing,the power capacity of the power supply system has become an essential resource to be *** many data centers use power oversubscription to make full use of the power capacity,there are unavoidable power supply risks associated with ***,how to improve the data center power capacity utilization while ensuring power supply security has become an important *** solve this problem,we first define it and propose a risk evaluation metric called Weighted Power Supply Risk(WPSRisk).Then,a method,named Hybrid Genetic algorithm with Ant Colony System(HGAACS),is proposed to improve power capacity utilization and reduce power supply risks by optimizing the server placement in the power supply *** uses historical power data of each server to find a better placement solution by population *** possesses not only the remarkable local search ability of Ant Colony System(ACS),but also enhances the global search capability by incorporating genetic operators from Genetic algorithm(GA).To verify the performance of HGAACS,we experimentally compare it with five other placement *** experimental results show that HGAACS can perform better than other algorithms in both improving power utilization and reducing the riskof powersupply system.

关键词： server placement power utilization power supply risk swarm intelligence algorithm

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